42 results about "Graph regularization" patented technology

The invention discloses a dual-channel label-guided multi-label feature selection method and system, and belongs to a feature engineering technology. The method mainly comprises the steps of obtaining a feature matrix and a positive label matrix of a multi-label data set, performing logic negation on the positive label matrix to generate a mirror image negative label matrix, and constructing a graph Laplacian matrix based on the feature matrix; constructing a multi-label model based on the preprocessed data, wherein an objective function of the multi-label model at least comprises a positive label regression loss item, a negative label regression loss item, a label alignment constraint item, a graph regularization item and a sparse constraint item; constructing an optimization function through relaxation processing constraint and in combination with a Lagrangian multiplier method, iteratively solving the objective function according to a KKT condition, and evaluating feature importance based on a projection matrix for associating features and positive tags after iterative convergence; according to the method, the requirement of multi-label learning for accurate and efficient feature screening is met, label information can be comprehensively utilized, the anti-interference capability is enhanced, and the efficiency is considered.

The invention relates to the technical field of price monitoring, in particular to a building material multi-source price anomaly detection method, which comprises the following steps: firstly, uniformly metering and pricing calibers, learning a conversion coefficient, and constructing a replaceable relation graph; multi-source distribution is aligned through optimal transmission, residual errors and shadow prices are obtained based on structure invariants and variational inequality, and abnormal evidences are formed through hypergraph propagation and persistent coherence; generating a valence band reference in combination with a convex hull method and distribution robust optimization under the equilibrium clearing of graph regularization; a feasible set is defined by price bands and constraints, a weighted maximum satisfactory model is constructed to position a minimum default set, a minimum correction suggestion is generated by using vector optimal transmission and packet sparsity, and an executable closed loop is realized through satisfactory model theory verification.

The invention discloses an overlapping community detection method, system and device and a medium, and particularly relates to the technical field of community detection, and the technical key points are as follows: extracting node information data from a pre-constructed adjacent matrix of an overlapping community, and inputting the node information data into a point mutual information function to calculate and obtain point mutual information; obtaining a superpoint mutual information matrix by combining the point mutual information with the hypergraph structure; constructing a target function by using the super-point mutual information matrix and a pre-constructed three-factor illegal matrix decomposition optimization model based on graph regularization; decomposing the super-point mutual information matrix in the objective function to obtain an indication matrix, and solving the indication matrix by using an alternating iteration method to obtain an optimal solution of the indication matrix; and detecting the overlapping community based on the optimal solution of the indication matrix to obtain an overlapping community detection result.

The invention discloses a node zero-trust trusted access method and device for a computing power network, and relates to the technical field of network security. And based on a zero-trust principle, credibility evaluation is carried out in a registration stage and a task execution stage of the computing power node. In the registration stage, a multi-source trust modeling mechanism fusing identity attributes, capability observation and organization reputation is constructed, and a Bayesian reasoning and graph regularization method is introduced to realize initial trust evaluation; in the operation stage, a multi-modal log anomaly detection method is designed, high-dimensional behavior modeling is carried out in combination with semantics, time, parameters and quantity characteristics, and the anomaly recognition capability is enhanced by utilizing long sequence dependence; and a dynamic trust adjustment strategy is provided by further combining time decay and a user feedback mechanism, so that continuous evolution and refined access control of node trust are realized. According to the method and the system, trusted access of the computing power nodes is realized by the scheduling system in the environment of the computing power network, and the safety and the reliability of the computing power network system are effectively improved.

The invention provides a ground penetrating radar clutter suppression method and system and a medium, the method highlights a texture change area through first-order difference, can accurately capture change information of echo intensity and suppress background fluctuation and noise, meanwhile, introduces graph regularization to ensure similarity of adjacent pixels, suppresses isolated noise points, and improves noise suppression efficiency. And the stability of the texture structure is enhanced. By means of the combination, clutters can be effectively removed when the method is used for processing complex backgrounds, and meanwhile key structure information is reserved. Complex echo texture data are projected to a singular value feature space through singular value decomposition, singular values with a large change rate are screened out for reconstruction, redundant interference can be effectively removed, and stable echo textures are reserved. According to the mechanism, when the method is used for processing multi-target, dispersed and irregular echo signals, target signals and clutters can be effectively separated, and the detection precision is influenced.

The invention discloses a feature selection method for multi-label prediction of diabetic complications, and relates to the technical field of machine learning and medical data processing. The method comprises the following steps: carrying out label layered independent weight calculation on original characteristics of diabetic complications and complication label data to obtain a label weight matrix of a plurality of complications; performing graph regularization optimization based on layered independent weights on the label weight matrix of the multiple complications, training a multi-label classification model, reducing irrelevant and redundant features in the multi-label classification model, and obtaining feature subsets with importance perception, high discrimination and robustness; and screening the original data of the diabetic complications by using the optimized multi-label classification model, calculating a feature contribution matrix, and for positive prediction labels, outputting the first three features of contribution values for constructing the features of diabetic complication prediction. According to the method, key features can be screened out, and the accuracy of model prediction is improved.

The application discloses a kind of double-channel label guide multi-label feature selection method and system, belong to feature engineering technique.Method mainly includes: obtaining the feature matrix and positive label matrix of multi-label data set, by performing logical negation to positive label matrix, generate mirror negative label matrix, and construct graph Laplacian matrix based on feature matrix;Based on the data after pre-processing, a multi-label model is constructed, and the objective function of the multi-label model includes at least positive label regression loss term, negative label regression loss term, label alignment constraint term, graph regularization term and sparse constraint term;The constraint is processed by relaxation, and the optimization function is constructed by combining the Lagrange multiplier method, and then the objective function is iteratively solved according to the KKT condition, and after iterative convergence, the feature importance is evaluated based on the projection matrix used to associate features and positive labels;The application meets the demand of multi-label learning for accurate and efficient feature selection, can fully utilize label information, enhance anti-interference ability and consider efficiency.

This disclosure relates to a method, apparatus, and product for full-waveform reflection inversion based on dynamic graphic warping, applicable to the field of seismic exploration technology. In this disclosure, seismic data of the target area is acquired; wavefield simulation is performed based on the seismic data to determine the background wavefield and scattered wavefield. Based on dynamic graphic warping, full-waveform reflection inversion is performed on the background wavefield and scattered wavefield to determine the inversion target model. The inversion target model is optimized to determine the target image of the target area. Determining the inversion target model through the dynamic graphic warping algorithm can improve the resolution of the inversion target model and reduce the model's ambiguity and computational load. Furthermore, iterative optimization of the inversion target model can improve imaging quality. Simultaneously, using the dynamic graphic warping algorithm can reduce the accuracy requirements of high wavenumber models, enabling more accurate imaging of complex geological features. Therefore, the accuracy of imaging is improved.

The application provides a double relaxation image classification method based on a width learning system, and steps are as follows: firstly, a feature data set and a corresponding class label matrix are acquired, and the feature data set generates width conversion features through a standard width learning network; secondly, a double relaxation technique and a graph regularization technique are introduced, and a double relaxation image classification optimization objective function based on the width conversion features is constructed; finally, the double relaxation image classification optimization objective function is solved by using iterative optimization, a classification result is obtained, and the classification result is evaluated. The manifold regularization technique is applied to the width learning network, and a double relaxation method is used to obtain greater freedom, so that the data geometric structure is mined, and the learning of the intra-class similarity realizes the relaxed regression of the target. The application has the characteristics of higher classification precision, relatively less training time, higher model flexibility and the like, and the introduction of the double relaxation method makes the model have stronger discrimination ability.

The invention discloses a remote sensing image sub-region rapid classification method based on dual-scale multi-graph regularization non-negative matrix factorization, and provides a dual-scale basis selection strategy and a multi-graph regularization non-negative matrix factorization model for overcoming the defects of complex scene adaptation, small sample learning and classification speed and accuracy balance in the prior art. Unsupervised subregion classification is realized through the steps of data preprocessing, double-scale seed set division, basis matrix construction, double information graph regularization item construction, model optimization solution and the like. According to the method, the classification number does not need to be predefined, the basis matrix representativeness is enhanced through dual-scale basis selection, and the classification precision and robustness are improved by combining a dual-graph regularization item and utilizing a manifold structure and discrimination information of data at the same time; the efficient optimization solution of the non-negative matrix factorization realizes the rapid classification of the sub-regions, and improves the accuracy and efficiency of the classification of the sub-regions of the remote sensing image.

The application relates to the technical field of nondestructive testing, and discloses an aircraft defect identification method and system based on tensor decomposition and an attention mechanism. The aircraft defect identification method based on tensor decomposition and the attention mechanism comprises the following steps: acquiring multi-modal data of an aircraft; constructing the multi-modal data into a four-order space-time-modal tensor, and generating a dynamic graph structure based on the modal features of the tensor; applying a mixed constraint when decomposing the four-order tensor to obtain a core tensor and a factor matrix; extracting features through multi-scale pooling, combining topological persistent homology and a gated attention mechanism to distribute weights, and realizing feature fusion; identifying a defect type based on the fused features, and locating a defect area by using a factor matrix gradient amplitude and a dynamic threshold. Through multi-modal data fusion, dynamic graph regularization constraint and mixed tensor decomposition technology, the application improves the detection sensitivity and positioning accuracy of small defects on the surface of the aircraft, and enhances the physical interpretability of features and the robustness of the algorithm to complex working conditions.

The application relates to the technical field of intelligent report forms, in particular to a financial report form intelligent generation method and system fusing a variational Gaussian process and reinforcement learning. The method establishes an initial report form template by collecting original business data, completes business feature clustering and semantic mapping to identify a business type by using hierarchical fuzzy clustering, constructs a financial relationship graph based on sparse representation graph embedding and performs abnormality identification to generate a financial data set, fuses variational Gaussian process regression and reinforcement learning to complete budget prediction, finally fills a template with budget results and performs structure reconstruction by a graph regularization autoencoder to generate a final available financial report form, and realizes intelligent report form automatic generation under digital finance. The application significantly improves the automation degree, prediction accuracy and report form generation efficiency of financial data processing.

This invention proposes a structurally enhanced discriminative width learning system for image classification. First, it introduces graph regularization based on class relationships to ensure that similar samples remain closely distributed in the projection space. Second, it adds intra-class divergence regularization to further enhance the aggregation of samples within the same class. Furthermore, it introduces inter-class divergence regularization to ensure that the centers of different classes are far apart in the feature space. Finally, in the output layer regression stage, it replaces the Frobenius norm regularization in ridge regression with sparse norm regularization to effectively suppress noise and redundant information. This image classification scheme, while maintaining the simplicity and efficiency of the optimization process, organically integrates three different types of structured information—intra-class structure, inter-class structure, and local manifold information—into a unified model, enabling the learned feature representations to possess stronger structure preservation and discriminative capabilities.

The invention discloses a wireless network time synchronization method and system, and a storage medium, and the method comprises the steps: collecting network node time data, forming an original sampling sequence, and generating a whole network clock observation matrix; based on the connection strength between each pair of network nodes, constructing a Laplacian matrix; on the basis of an objective function of a graph regularization robust low-rank decomposition model, carrying out joint optimization solution on the whole-network clock observation matrix, and alternately updating a low-rank matrix and a sparse matrix; after convergence conditions are met, drift and offset correction values are calculated, and correction parameters are generated; and performing wireless network time synchronization according to the correction parameter. The problem that an existing synchronization algorithm is unstable in precision under the conditions of strong interference, packet loss and dynamic topology is solved, and unification of high precision and high robustness of clocks of the whole network is achieved.

A low-quality multi-view news data anchor graph regular division method based on diffusion completion belongs to the field of data division in low-quality multi-view news data, and comprises the following steps: firstly, inputting low-quality news data of each view and a corresponding similarity relation matrix into a heterogeneous relation convolutional network; to obtain a low-dimensional embedded representation of each view. Then, the low-dimensional embedded representation uses forward noise adding and reverse noise reduction processes of a conditional diffusion model to obtain predicted noise, and missing samples are complemented through the predicted noise; and then, an anchor point diagram is constructed by using the similarity between the complemented embedded sample and the anchor points, soft clustering distribution is obtained for the constructed anchor point diagram through an orthogonal normalization layer, and discriminative feature representation is obtained through anchor graph regularization constraint. And finally, soft clustering distribution is constrained by using a tensor Schatten p-norm so as to fully mine complementarity information and a sparse structure between the views.

The invention discloses a structure constraint low-rank sparse representation arrhythmia classification method based on tag embedded graph regularization, and belongs to the field of electrocardiosignal processing. The method comprises the following steps: constructing a training data set and a label matrix; a graph Laplacian matrix is constructed based on the label matrix, local structure information and label information of the electrocardiosignals are fully utilized to construct a low-rank sparse representation model fusing a local constraint item and a graph regularization item, and a discriminative dictionary is obtained through dictionary learning; and finally, performing low-rank sparse representation on a test sample by using the learned dictionary, and completing classification according to a minimum reconstruction error principle. According to the method, by introducing a tag embedded graph regularization item, the model keeps a local manifold structure of data and enhances intra-class compactness and inter-class separability in a sparse representation learning process, so that the classification accuracy of electrocardiosignals, particularly arrhythmia classes, is remarkably improved.

The invention discloses a feature representation method for realizing multi-view image alignment based on a dynamic graph. The feature representation method comprises the following steps: 1) initializing a multi-view structure; 2) common dynamic graph learning; 3) constructing a joint optimization model based on dynamic graph regularization; 4) solving a common source representation S and a projection matrix by minimizing projection features of each view; 5) initializing the dynamic similar matrix during the first iteration; 6) expanding a matrix dimension and constructing a coefficient matrix; 7) expanding and converting the expanded target function into a matrix trace form; 8) deducing a gradient expression of a dynamic graph learning objective function; and 9) setting an iteration stop criterion of the gradient descent method, and finally outputting an optimization result. Through combined utilization of the structure information of each feature view, adverse effects caused by insufficient samples or noise interference are reduced, and the image alignment precision in severe construction environments such as dark light is effectively improved.

This invention relates to the field of multi-sensor measurement and information fusion technology, and particularly to an intelligent spatial dynamic monitoring method based on multi-sensor collaboration, comprising the following steps: S1 Measurement state modeling and baseline generation; S2 Reference clock alignment and acquisition cycle tuning; S3 Anomaly and uncertainty-driven sampling and weighted linkage; S4 Constraint-aware multi-source fusion and uncertainty assessment; S5 Multi-evidence fault determination and graph regularization completion; S6 Measurement result indication and recording. This invention establishes a reference clock and maps each channel to a unified time axis, combines time quality and scene dynamic adaptive tuning sampling (including frequency boosting at key nodes), and outputs results with uncertainty through a two-stage fusion method of quality-aware weighted + gated network, supplemented by multi-evidence fault determination, graph regularization completion, and full-process recording, comprehensively achieving time-consistent, continuous, and traceable spatial dynamic monitoring.

The present application discloses a method, device, electronic device and storage medium for recognizing chronic disease comorbidity patterns based on relaxed constrained symmetric low-rank representation. The method acquires medical data resources and uses the medical data resources to construct a chronic disease comorbidity network. The chronic disease comorbidity network is used to describe the correlation and influence between diseases. Low-rank representation learning is performed on the chronic disease comorbidity network to mine and output the community structure therein. The community partitioning mechanism is used to identify the community structure in the comorbidity network and explore the comorbidity pattern. The introduction of relaxed symmetric constraints in the learning objectives can well perceive the inherent symmetric structural characteristics of the network, and the introduction of graph regularization technology that captures local topological features effectively maintains the inherent geometric structural characteristics of the network.

The invention discloses a daily peak load prediction method and system based on graph regularization regression. The method comprises the following steps: acquiring multi-source historical data for daily peak load prediction; constructing multi-source features based on the multi-source historical data; establishing a feature association graph based on the multi-source features, and constructing a graph Laplacian matrix based on the feature association graph; constructing a graph regularization regression model based on the graph Laplacian matrix; solving the graph regularization regression model, determining an optimal parameter of the model, and determining an optimal model based on the optimal parameter; and predicting a future daily peak load based on the optimal model. According to the method, the daily peak load prediction model based on graph structure regularization regression is constructed, efficient and stable model solving is realized through the association relationship between feature graph modeling features and in combination with an alternating direction multiplier method ADMM, the future daily maximum load is predicted with high precision based on the model, the load peak can be accurately predicted in advance, and the prediction efficiency is improved. And important decision support is provided for power dispatching operation.

The application discloses a feature selection method for multi-label prediction of diabetic complications, and relates to the technical fields of machine learning and medical data processing. The method comprises the following steps: performing label stratified independent weight calculation on original features and complication label data of the diabetic complications to obtain a plurality of label weight matrices of the complications; performing graph regularization optimization based on the stratified independent weight on the label weight matrices of the complications, training a multi-label classification model, reducing irrelevant and redundant features in the multi-label classification model, and obtaining an important feature subset which is important, has high discriminability and is robust; filtering original data of the diabetic complications by using the optimized multi-label classification model, calculating a feature contribution matrix, and outputting features with top three contribution values for positive prediction labels to construct features for prediction of the diabetic complications. The method can screen out key features and improve the prediction accuracy of the model.

The invention relates to the technical field of leakage detection, in particular to a natural gas pipeline leakage detection method based on multi-modal data fusion, and the method comprises the steps: constructing a pipeline leakage simulation experiment platform, and obtaining a pipeline leakage image and text; splicing the image and the text to construct a data set; training an ELM-LPP model by using the data set, constructing a basic ELM model, optimizing an ELM model objective function by using an LPP algorithm, and introducing an adaptive feature weighting matrix, a combined graph regularization item and an F norm regularization item; and optimizing a hidden layer neuron weight vector and a hyper-parameter of the ELM model by taking minimization of an objective function as an objective. According to the invention, the problem of insufficient precision in detection of multi-modal data in the prior art is solved.

The invention provides a graph regularization fuzzy C-means overlapping clustering method based on network embedding, belongs to the technical field of data overlapping clustering, and solves the technical problem that an existing overlapping clustering method neglects feature sparsity of network data and neglects inherent structure information of the data to cause overfitting. According to the technical scheme, the method comprises the steps that firstly, an AROPE algorithm is used for converting an adjacent matrix into a multi-order similarity matrix; 2, forming a basic model; 3, combining the fuzzy C mean value with the basic model to form a fusion model; and a fourth step of obtaining the overlapping cluster of the final network data. The method has the beneficial effects that the feature sparsity of network data is overcome, and the mining performance of overlapping categories with complex structures is improved.

The application discloses a miRNA and disease association prediction method and device and a storage medium, and relates to the field of miRNA and disease association prediction.The method comprises the following steps: determining a miRNA and disease association matrix, a miRNA similarity matrix and a disease similarity matrix; constructing a heterogeneous network graph about the miRNA and disease association; calculating a Laplacian graph regularization matrix of the miRNA / disease similarity; constructing a graph autoencoder, wherein the encoder and the decoder of the graph autoencoder both adopt two layers of GAT+GCN; taking the heterogeneous association matrix, the feature space matrix and the Laplacian graph regularization matrix as inputs of the graph autoencoder to obtain an output matrix; calculating a loss value according to the output matrix and the miRNA and disease association matrix; training the graph autoencoder according to the loss value, and obtaining a miRNA and disease association prediction result.The application improves the performance of association prediction.

The application relates to the field of image clustering, and discloses an image clustering method fusing low-rank kernel learning and adaptive hypergraph, which comprises the following steps: S1, acquiring image data; S2, iteratively constructing a hypergraph and updating a hypergraph Laplacian matrix, a coefficient matrix, a consensus kernel matrix and a candidate kernel weight through low-rank consensus kernel learning, image data self-expression in a kernel space and adaptive hypergraph regularization until a convergence condition is reached; S3, constructing a similarity matrix by applying the coefficient matrix obtained in the step S2; and S4, calling a spectral clustering algorithm by applying the similarity matrix obtained in the step S3 to obtain a clustering result. The method integrates adaptive hypergraph regularization, data self-expression in a kernel space and low-rank kernel learning into one framework, realizes the alternating guidance and dynamic promotion of the three, optimally utilizes the sample correlation under different candidate kernels, mines more stable high-order relationships, and thus more deeply mines the internal structure of data.

The invention discloses a computer vision image recognition method and system based on an unmanned aerial vehicle. The method comprises the steps that a conventional RGB image and a light field image of a target scene are collected through a vision sensor; preprocessing the image to generate depth detail features and multi-view RGB features; performing feature decoupling on the depth detail features and the multi-view RGB features, and separating out shared feature representation, depth modal specific feature representation and RGB modal specific feature representation; inputting the feature representations and the context information of the target scene into a context-aware dynamic attention network, and generating fusion weights corresponding to the feature representations; based on the fusion weight, performing weighted fusion on each feature representation, and constructing a consensus feature matrix through an adaptive low-rank Laplacian model; and feature refining is carried out based on the sparse orthogonal neural network, classification is carried out through the graph regularization classifier, a target recognition result is obtained, and high-precision and high-reliability target recognition is realized.

This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provides a method for learning robust sensor-specific autoencoder based fusion model by utilizing a graph structure to perform label propagation and correction. In the disclosed Graph regularized AutoFuse (GAF) method latent representation for each sensor is learnt using the sensor-specific autoencoders. Further these latent representations are combined and fed to a classifier for multi-class classification. The disclosure presents a joint optimization formulation for multi-sensor fusion where label propagation and correction, sensor-specific learning and classification are executed together.

The application discloses a multi-view concept decomposition clustering method based on consensus learning and double-graph regularization, and belongs to the technical field of data processing, and comprises the following steps: S1, generating a consensus base matrix for all views, and constructing a first target function; S2, constructing a sample similarity graph for each view, taking the sample similarity graph as a regularization constraint, and constructing a second target function based on the first target function; S3, constructing a multi-view concept decomposition clustering model based on direct consensus learning and double-graph regularization according to the second target function; and S4, solving the multi-view concept decomposition clustering model to obtain a classification result. By directly learning a unified consensus representation matrix, the application discards a complex multi-view alignment process in a traditional method, significantly simplifies a model structure, reduces a calculation complexity, and makes optimization solving more efficient.

The invention relates to the technical field of electric power prediction, and discloses an edge side source load space-time prediction method and system based on multi-source data fusion, and the method comprises the steps: carrying out the causal alignment and preprocessing of multi-source heterogeneous data, generating unified frequency domain and time domain feature representation, and specifically carrying out the data quality monitoring, multi-scale coding and frequency domain enhancement processing; based on a decoder structure and a frequency domain gating hybrid expert mechanism, performing multi-band modeling on photovoltaic power, and outputting point prediction and quantile intervals in combination with frequency domain residual refinement and physical constraint; a dynamic graph structure is constructed based on power distribution topology and multi-source attributes, graph convolution and a decoder attention mechanism are fused, space-time prediction and decomposition of load power are realized, and spatial smoothness and prediction precision are improved through consistency optimization and graph regularization; according to the method, the problem of error transmission caused by multi-source data frequency heterogeneity and photovoltaic power generation and load coupling in an existing edge side source load space-time prediction scene is solved.

The present invention discloses a personalized hypertensive nephropathy risk assessment method and system based on graph convolutional network, the assessment method comprising: collecting modal data related to hypertensive nephropathy and performing preprocessing; constructing a patient similarity graph; performing multi-source information fusion and domain knowledge integration; performing sparse processing on the graph structure and normalizing the adjacency matrix; capturing the evolution trend of patient characteristics over time through temporal graph convolutional network and long short-term memory network modeling; strengthening the transmission of important features through self-attention mechanism and graph regularization term; adopting multi-head attention mechanism to capture different types of relationship patterns; optimizing model parameters using Adam optimization algorithm and regularization method; and optimizing risk assessment results using weighted stacking and adaptive attention mechanism. The present invention adopts the above-mentioned risk assessment method to achieve accurate assessment of hypertensive nephropathy risk by constructing a multi-dimensional, dynamically changing patient similarity graph.