30 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 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 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 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 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 application discloses a kind of node zero trust trusted access methods and devices for computing power network, it is related to network security technical field.Based on the principle of zero trust, it is carried out in the registration phase and task execution phase of computing power node Trust evaluation.In the registration phase, the multi-source trust modeling mechanism of fusing identity attribute, capability observation and organization reputation is constructed, and the initial trust evaluation is realized by introducing Bayesian inference and graph regularization method;In the running phase, a multi-modal log anomaly detection method is designed, high-dimensional behavior modeling is carried out combined with semantic, time, parameter and quantity characteristics, and the abnormal identification ability is enhanced by using long sequence dependence;Further combined with time decay and user feedback mechanism, a dynamic trust adjustment strategy is proposed, to realize the continuous evolution of node trust and fine access control.The application realizes the trusted access of computing power node by scheduling system in the environment of computing power network, effectively improves the security and reliability of computing power network system.

The application discloses a graph regularization dynamic sampling method for cardiovascular disease diagnosis, and comprises the following steps: S1, based on a classical AdaBoost framework, an adaptive weight updating mechanism based on sample confidence is designed; S2, an improved dynamic undersampling and dynamic oversampling module is integrated in the iteration process of AdaBoost; S3, after dynamic undersampling and dynamic oversampling are completed, a graph regularization dimension reduction strategy is introduced to reduce the dimension of the training samples after dynamic sampling; and S4, through the above steps, a cardiovascular disease diagnosis model is established, and the graph regularization dynamic sampling for cardiovascular disease diagnosis is realized. The application overcomes the problems that the classes in the cardiovascular disease data set are unbalanced and key features are difficult to be effectively identified.

This invention discloses a graph regularized slow feature analysis method for infrared nondestructive testing data, comprising: acquiring an infrared image sequence and preprocessing the infrared image sequence; subsequently, constructing a pixel-level graph Laplacian matrix using a block-parallel strategy, calculating pixel adjacency relationships within local image blocks based on bilateral weights of spatial distance and temperature similarity, and merging them to generate a global sparse adjacency matrix through a maximum value fusion strategy in overlapping regions; constructing a joint objective function that fuses temporal slowness constraints and spatial smoothness constraints, and optimizing the solution using an adaptive dual whitening and matrix trace ratio dynamic adjustment strategy; finally, solving the objective function to extract slow feature components and reconstructing the infrared image. This invention overcomes the computational bottleneck of high-dimensional thermal imaging data through an efficient computational strategy, and utilizes a physically-aware graph regularized slow feature analysis algorithm to achieve feature decoupling of multiple types of defects under spatiotemporal constraints, significantly enhancing the thermal feature edges and signal-to-noise ratio of defects.

The invention relates to the technical field of image detection, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a video frame extraction method, device and equipment based on self-adaptive regularization and a medium. Performing frame normalization processing on the plurality of initial video frames to generate a plurality of standard video frames; extracting spatial features and time features of the standard video frames, and constructing a multi-resolution video feature map of the standard video frames according to the spatial features and the time features; performing multi-head self-attention regularization calculation on the multi-resolution video feature map to obtain a plurality of map regularization coefficients; performing adaptive regularization constraint processing on the standard video frame according to the image regularization coefficient to generate a plurality of regularization video frames; and performing target frame selection on the plurality of regularized video frames to obtain a target video frame corresponding to the initial video stream data. According to the invention, the accuracy of video frame extraction can be improved.

The application discloses a kind of based on graph regularization CNN-BiLSTM multi-sensor fault diagnosis method, medium and equipment, it is related to the computer fault diagnosis system field based on specific calculation model.The method comprises the following steps: collecting the condition monitoring data of multi-sensor, and the data collected is preprocessed, to obtain training set, verification set and test set;Establish CNN-BiLSTM network, graph regularization item is added in the nearest fully connected layer of distance classifier of CNN-BiLSTM network, complete GR-CNN-BiLSTM model construction;The data in training set is used to train GR-CNN-BiLSTM model, the data in verification set is used to evaluate GR-CNN-BiLSTM model, and the network parameter when the performance of GR-CNN-BiLSTM model is optimal is obtained;The data of test set is input into the GR-CNN-BiLSTM model of optimal performance and carries out fault diagnosis, to obtain fault diagnosis result.The application improves training efficiency and diagnostic accuracy, overcome the low training efficiency of existing deep graph regularization fault diagnosis method.

The invention belongs to the technical field of deep learning and medical image analysis, and particularly relates to a semi-supervised non-negative representation esophageal cancer image clustering method based on a matrix element visual angle, which comprises the following steps: S1, constructing a symmetric similar matrix based on an original matrix, and reconstructing the symmetric similar matrix; S2, constructing an objective function; S3, optimizing the objective function; the invention provides a semi-supervised graph regularization non-negative representation clustering method based on element level analysis. By applying the algorithm to the esophageal cancer image data feature extraction and clustering recognition process, the method achieves the precise clustering division of the image data, improves the nonlinear expression capability and training stability of the model, and improves the accuracy of esophageal cancer image auxiliary diagnosis.

The invention provides a multi-label feature selection method and device and electronic equipment, and the scheme comprises the steps: generating a feature selection coefficient matrix based on a pre-constructed feature selection model according to a sample feature matrix and discrete labels; based on the feature selection coefficient matrix, determining a global shared feature set from the sample feature matrix according to the global importance coefficient and a preset quantile threshold; on the basis of a preset personalized threshold value, according to the sample feature matrix and the global shared feature set, determining a personalized feature set corresponding to each tag in the discrete tags; and determining a multi-label feature set according to the global shared feature set and the personalized feature set. According to the scheme, through a global sharing and personalized feature two-channel screening mechanism, in combination with graph regularization and tag relaxation optimization, the precision and robustness of multi-tag feature selection are remarkably improved, a high-discrimination feature subset is realized, and the method is suitable for a complex multi-tag scene.

The application provides a hybrid hypergraph regularization semi-supervised cross-modal hashing method based on concept decomposition and belongs to the technical field of computers.The application solves the training problem of a large amount of unlabeled data in cross-modal retrieval, fully excavates the hybrid hypergraph high-order relationship between labeled data and unlabeled data, improves the model retrieval capability, and comprises the following steps: concept decomposition-based public semantic representation learning, cross-modal hybrid hypergraph construction, classification loss measurement of the labeled data, overall objective function construction and optimization, and learning of a quantization rotation matrix and a cross-modal hashing function, and finally, cross-modal cross-retrieval is performed by converting the cross-modal data into binary hash codes through the hash function. The application can effectively utilize the unlabeled data for semi-supervised cross-modal hash retrieval.