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15 results about "Manifold regularization" patented technology
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In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this assumption, a manifold regularization algorithm can use unlabeled data to inform where the learned function is allowed to change quickly and where it is not, using an extension of the technique of Tikhonov regularization. Manifold regularization algorithms can extend supervised learning algorithms in semi-supervised learning and transductive learning settings, where unlabeled data are available. The technique has been used for applications including medical imaging, geographical imaging, and object recognition.
Photoplethysmography identity recognition method and system
PendingCN121191202ABiological modelsSubcutaneous biometric featuresSparse constraintIdentity recognition
The invention provides a photoelectric volume pulse wave identity recognition method and system, and relates to the technical field of identity recognition, and the method comprises the steps: obtaining a to-be-recognized PPG signal; and inputting the PPG signals into a trained identification model, firstly extracting linear features and nonlinear features, then projecting the features fused by the two features into a feature space by using a learned discriminant projection matrix to obtain multi-view features, and finally classifying the PPG signals by using the multi-view features to obtain an identity identification result. According to the method, manifold regularization and inter-class-error double sparse constraints are combined, the problems of intra-class discretization and inter-class overlapping of a linear model are solved, a graph structure learning method for adaptive local density adjustment is provided, and the manifold modeling precision of non-stationary PPG signals is improved.
Owner:XINJIANG UNIVERSITY
Low-rank multi-modal remote sensing image clustering method and device on superpixel manifold, and storage medium
The invention discloses a low-rank multi-modal remote sensing image clustering method and device on a superpixel manifold and a storage medium, and relates to the technical field of multi-modal remote sensing image clustering. The method comprises the following steps: splicing a multi-modal remote sensing image along a channel direction, segmenting the spliced image into a plurality of sub-regions through superpixel segmentation, and solving a mean value for each sub-region to obtain a multi-modal superpixel; embedding the Laplacian matrix of the multi-modal superpixels into manifold regularization about the superpixel clustering matrix, and capturing a local manifold structure of the multi-modal superpixels; under the constraint of manifold regularization, constructing a low-rank reconstruction model of a product of a single-mode clustering matrix and a unified clustering matrix; initializing and alternately optimizing the single-mode clustering matrix and the unified clustering matrix by using fuzzy clustering; and analyzing the super-pixel clustering result, and mapping the super-pixel clustering result into a clustering result of the original image. According to the invention, the accuracy and efficiency of remote sensing image clustering are improved.
Owner:CHENGDU TECH UNIV
Adaptive multi-domain disentanglement manifold network-based fault detection method for multi-working condition of inverter system
This invention discloses a multi-condition fault detection method for inverter systems based on adaptive multi-domain unentangled manifold networks. Addressing the issue of multi-condition operation and scarce samples for some conditions in inverter systems, the method first constructs a dual-path encoder comprising a shared encoder and a private encoder to explicitly separate common features that do not change with the operating conditions from unique features that do change with the operating conditions. Then, an adaptive weighting mechanism based on reconstruction differences is used to quantify the reference value of the source domains, selectively increasing the proportion of highly correlated source domains to suppress negative migration. Next, manifold regularization techniques are used to constrain the latent space, constructing a geometrically coherent healthy manifold benchmark. Finally, the distance of the test sample projected onto the local manifold space is calculated as a fault monitoring index to achieve the fault detection objective for the inverter system.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
A manifold constraint-based elastic body erosion profile multi-modal self-attention fusion prediction method
The application discloses a kind of based on manifold constraint's elastic body erosion profile multimodal self-attention fusion prediction method, comprising: data set construction and pre-processing;Build multimodal self-attention fusion network model;Based on the geometric manifold characteristics and physical evolution law of elastic body erosion profile, build the dual compound constraint loss function including data loss, geometric manifold regularization loss and physical consistency loss;Model training and hyperparameter optimization;The microstructure image and physical parameter under the working condition to be predicted are input into the trained model, and the discrete coordinate points and continuous profile curve of the elastic body erosion profile are output, and the importance of different input features to the prediction result is quantified by the integrated explainability analysis module.The application realizes the high-precision and rapid prediction of the elastic body erosion profile, and has the advantages of strong generalization ability and explainability, which provides important technical support for the completion of ammunition warhead design, protection engineering optimization and damage efficiency evaluation tasks.
Owner:NANJING UNIV OF SCI & TECH
A mashup service multi-label classification method based on double manifold regularization width learning
ActiveCN116150669BNatural language data processingEnergy efficient computingActivation functionMulti-label classification
The application provides a Mashup service multi-label classification method based on double manifold regularization width learning, mainly comprising: using a hidden Dirichlet distribution topic model to extract features from preprocessed Mashup description documents; linearly mapping a Mashup description document topic feature matrix into n groups of feature nodes respectively; processing the feature nodes through an activation function to generate enhanced nodes; splicing the feature nodes and the enhanced nodes to generate enhanced feature nodes as the input of the model; constructing a target function of a Mashup service multi-label classification model based on double manifold regularization width learning; using a least square method to solve the target function to obtain a weight matrix of a double manifold regularization width learning network; obtaining a description document of a test Mashup service and sending it into a trained model to predict a multi-label classification result. The application improves a width learning model by using double manifold regularization, and realizes a Mashup service multi-label classification function by using an improved BLS model.
Owner:DALIAN MARITIME UNIVERSITY
Balanced multi-view clustering method and system for discrete weighted pseudo labels
The invention provides a balanced multi-view clustering method and system for discrete weighted pseudo tags, and relates to the technical field of multi-view clustering analysis, and the method comprises the steps: firstly obtaining a multi-view data set; then, according to the multi-view data set, constructing a weighted label indication matrix of the multi-view data set; constructing a balanced clustering model according to the multi-view data set and the weighted label indication matrix, and training the balanced clustering model to obtain a trained balanced clustering model; and finally, inputting multi-view data to be clustered into the trained balanced clustering model to obtain a clustering result. According to the method, the clustering model is normalized through balance constraint, the discrete pseudo-label matrix is weighted to avoid trivial solutions, norms are introduced to realize feature selection, manifold regularization is combined to relieve overfitting, and the matrix is circularly updated, so that higher clustering efficiency, accuracy, balance and feature selection capability are realized.
Owner:GUANGDONG UNIV OF TECH
Dual relaxation image classification method based on a width learning system
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.
Owner:HENAN UNIVERSITY OF TECHNOLOGY
A graphics card workstation intelligent configuration method based on big data analysis
PendingCN122173150AImprove stabilityImprove adaptabilityBootstrappingEnergy efficient computingGraphic cardData set
This invention discloses an intelligent configuration method for graphics card workstations based on big data analysis, comprising: S1, collecting data and features to generate an original holographic runtime dataset; S2, constructing a three-dimensional heterogeneous tensor matrix, extracting the kernel tensor and factor matrix using HOSVD decomposition, and generating a deep computing power feature vector; S3, constructing a computing power feature profile library, calculating similarity to identify task load pressure patterns, and outputting a target task feature profile; S4, generating a preliminary configuration scheme using an improved WGAN-GP model, and introducing a manifold regularization projection mechanism to filter candidate configuration subsets; S5, parsing and obtaining hard constraints, and matching the final configuration parameters; S6, issuing configuration parameters and collecting real-time load data for dynamic correction; S7, constructing incremental samples to feed back into the original dataset, and performing continuous iterative self-optimization. This invention achieves accurate matching of graphics card resources and task load, effectively improving the computing power utilization efficiency of the workstation.
Owner:NANJING YAOZHUO NEW MATERIAL TECHNOLOGY CO LTD
Robust physical extreme learning machine modeling method for lithium battery temperature prediction
The embodiment of the invention provides a robust physical extreme learning machine modeling method for lithium battery temperature prediction, and belongs to the technical field of data processing, and the method specifically comprises the steps: 1, building a lithium battery heat conduction model corresponding to a lithium battery of an electric camellia fruit picking machine based on the law of conservation of energy; 2, constructing a lithium battery temperature prediction model fused with physical data, substituting the model into the lithium battery heat conduction model, and constructing a weight optimization model for lithium battery temperature prediction; 3, on the basis of the weight optimization model, introducing a structure risk item, an error item based on adaptive error weighting and a manifold regularization item, and constructing a robust optimization model for lithium battery temperature prediction; and 4, by constructing a Lagrange function and utilizing a KKT condition to solve the robust optimization model, obtaining an analytic solution of an output weight vector, and reconstructing temperature field distribution of the lithium battery according to the analytic solution. Through the scheme of the invention, the prediction accuracy and robustness are improved.
Owner:CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
A hypergraph representation method of brain functional network
This invention discloses a hypergraph representation method for brain functional networks. The steps include: preprocessing resting-state functional magnetic resonance imaging (fMRI) to obtain time series data for all brain regions; dividing the entire time series into multiple overlapping sub-sequence segments using a sliding window; constructing a dynamic brain functional network and transforming it into an optimization model; constructing a hypergraph of the dynamic brain functional network using the nearest neighbor algorithm; dynamically modifying the hypergraph structure through convolution operations and extracting features to obtain a new dynamic hypergraph; extracting the Laplacian matrix of the dynamic hypergraph; constructing the manifold regularization term of the Laplacian matrix and simultaneously introducing the manifold regularization term and the L1 norm regularization term into the optimization model to obtain the hypergraph representation of the brain functional network. This invention is used to represent functional interactions and higher-order relationships between multiple brain regions, determine discriminative brain functional network classification features, and effectively improve the classification performance of brain disease features.
Owner:CHANGZHOU UNIV
Hyperspectral image classification method of semantic-guided kernel low-rank sparse preserving projection
PendingCN121937762AImprove class separabilityOvercoming the limitations of fixed neighborhood representationClimate change adaptationCharacter and pattern recognitionImaging processingKernel method
The invention belongs to the technical field of image processing, and particularly relates to a hyper-spectral image classification method for semantic-guided kernel low-rank sparse preserving projection, which comprises the following steps: firstly, acquiring a training sample set and a test sample set of a hyper-spectral image, and calculating a kernel matrix and a cross kernel matrix of the hyper-spectral image; further constructing a kernel method dimension reduction model objective function fusing a sparse reconstruction error term, a sparse regularization term, a semantic guidance low-rank constraint term and a spatial adaptive manifold regularization term; the complex objective function is efficiently solved by adopting an alternating direction multiplier method, and an optimal projection matrix is finally obtained by introducing an auxiliary variable and alternately updating a projection matrix coefficient, a sparse coding matrix and other variables; and mapping the test sample to a low-dimensional feature space by using the projection matrix so as to finish classification. According to the method, through semantic guidance and spatial-spectral information adaptive fusion, the discrimination ability and classification precision of dimension reduction features are significantly improved.
Owner:ANHUI UNIV OF SCI & TECH
Network traffic abnormal behavior identification method based on deep learning
The invention discloses a network traffic abnormal behavior identification method based on deep learning, and relates to the technical field of network security, and the method comprises the steps: constructing a traffic state matrix through traffic sample features, extracting a high-frequency traffic slice, and calculating a maximum Lyapunov index to generate a self-adaptive numerical integration step length; dynamically generating a channel mask vector by combining the power spectrum entropy of the frequency domain transformation; according to the method, an anomaly perception model containing flow inertia and a frequency domain analysis branch is constructed, an inertia branch executes Euler discretization operation by utilizing integral step length to capture cross-scale time domain dynamic characteristics, and a frequency domain branch accurately filters mimicry noise through mask vector gating and an activation energy sorting mechanism; and jointly training the model by using the total loss function fusing manifold regularization and elastic filtering, and outputting a judgment result. According to the method, numerical divergence under chaotic burst traffic is effectively overcome, and the recognition robustness in the face of unknown bandwidth attacks and feature cheating is remarkably improved.
Owner:SHAANXI SCI TECH UNIV
Textile multi-modal feature selection method and system based on hessian manifold regularization
The application discloses a textile multi-modal feature selection method and system based on a Hessian manifold regularization, and relates to the technical field of computer vision, which comprises the following steps: constructing multi-modal data by a jacquard chart image and a process single parameter, and obtaining each modality pseudo label matrix through subspace projection; introducing L2, 1 norm sparse constraint and Hessian manifold regularization to maintain local curvature structure and improve sparsity; generating cross-modal shared pseudo labels by using adaptive weighted fusion, and optimizing in cooperation to enhance consistency; further tensorizing the pseudo labels, applying tensor kernel norm low rank constraint, and taking into account modality commonality and specificity; finally, all components are optimized in a unified framework, and a high discriminability and structure robust feature subset is output. The application realizes efficient feature selection without manual annotation by fusing subspace learning, Hessian manifold regularization and tensor kernel norm analysis.
Owner:HUAQIAO UNIVERSITY
Textile multi-modal feature selection method and system based on Hessian manifold regularization
The invention discloses a textile multi-modal feature selection method and system based on Hessian manifold regularization, and relates to the technical field of computer vision, and the method comprises the steps: constructing multi-modal data through a Jacquard graph image and a process single parameter, and obtaining a pseudo label matrix of each modal through subspace projection; introducing L, norm sparse constraint and Hessian manifold regularization, keeping a local curvature structure and improving sparsity; self-adaptive weighted fusion is adopted to generate a cross-modal shared pseudo label, and collaborative optimization is carried out to enhance consistency; further performing tensorization on the pseudo tag, applying tensor nuclear norm low-rank constraint, and considering modal generality and specificity; and finally, all components are jointly optimized in a unified framework, and a feature subset with high discrimination and robust structure is output. According to the method, through fusion subspace learning, Hessian manifold regularization and tensor nuclear norm analysis, efficient feature selection is realized without manual annotation.
Owner:HUAQIAO UNIVERSITY
An opinion analysis method based on LNMF
This invention provides a public opinion analysis method based on LNMF, comprising: Step S1, collecting comment texts and associated metadata of public opinion events from target social media platforms to form multiple samples, and constructing a public opinion data matrix through feature extraction and normalization; Step S2, calculating the similarity between samples based on the public opinion data matrix to construct a Laplacian matrix reflecting the local geometric structure of the data; Step S3, constructing a manifold regularization term using the Laplacian matrix, decomposing the public opinion data matrix V into the product of a coefficient matrix and a basis matrix, and jointly optimizing the coefficient matrix and the basis matrix by minimizing the objective function that incorporates the manifold regularization term; Step S4, performing cluster analysis on the dimensionality-reduced feature representation based on the optimized coefficient matrix, and calculating the comprehensive influence index of the public opinion event based on the clustering results, comment texts, and associated metadata. The beneficial effect is that this invention enables more accurate, stable, and rapid analysis and insight into public opinion.
Owner:NINGBO DAHONGYING UNIV