28 results about "Matrix similarity" patented technology

The embodiment of the invention provides a multi-view clustering method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. Obtaining a view data set corresponding to each view; based on the similarity between the feature elements in each view data set, constructing a similar matrix of each view data set; multiplying the similar matrixes of the plurality of view data sets to obtain a consistency matrix, and reconstructing the similar matrix of each view data set based on the consistency matrix to obtain a reconstructed similar matrix; and performing multi-view clustering based on the plurality of reconstructed similar matrixes to obtain a clustering result, the clustering result being used for indicating a clustering label corresponding to each sample. Through the above mode, the complementary information of the consistency matrix is utilized to maintain the stability of the clustering boundary, the misjudgment and missed judgment caused by the missing of the sample information in the view are reduced, and the accuracy of the multi-view clustering result is improved.

The application discloses a kind of passive cross-domain adaptive image classification methods based on multi-modal pre-training model, it is related to transfer learning technical field, first by quantitative estimation target domain data on source model existing category similarity matrix;Then the category similarity matrix of source model is introduced into CLIP (multi-modal pre-training model), directional generation confusion category text feature is used for classification, and the corresponding classification result is obtained;Then the classification result of source model and the classification result of CLIP are weighted, and the output of two models is respectively aligned with the weighted result;Again, the category feature center library of each model is calculated according to the category similarity matrix;Finally, the projection of input image on two feature center libraries is aligned using attention network to realize the alignment of feature space.The application dynamically estimates the similarity degree between categories in downstream task from the angle of model, learns using the complementary information between models, and realizes the fine-grained classification between similar categories, and has certain explainability.

The invention relates to the technical field of power grid dispatching, in particular to a power distribution network dispatching optimization method and device under participation of an electric vehicle cluster. The method comprises the following steps: collecting related data of a vehicle and a charging point through a monitoring device; the total waiting time of the vehicle is determined according to the time when the vehicle arrives at the charging point, the waiting time after the vehicle arrives at the charging point and the charging time of the vehicle at the charging point; calculating the load unbalance degree of the charging points according to the load difference of the charging points and the variation coefficient; feature vectors are constructed, the similarity of the vehicles is calculated based on the feature vectors, and a similar matrix is constructed; acquiring a cluster based on similar matrix clustering, and acquiring a penalty coefficient and conflict penalty according to similarity; and constructing a fitness function based on the total waiting time, the load unbalance degree and the conflict penalty, thereby determining a power distribution scheduling scheme. The problem of unbalanced load of the power distribution network is solved.

The utility model provides a kind of power station parameter matrix analysis method, belongs to power station fault diagnosis technical field, solve how to design a kind of power station parameter matrix analysis method, solve the numerous power station parameters, data effective information density is low, lead to the problem that fault cause analysis is difficult, and the massive data stream generated occupies a large amount of storage space, by power station parameter data matrixization representation, constructs parameter data matrix, by matrix operation, the rank of matrix is as quantization index, effectively distinguishes the information density of parameter, the correlation between different parameters can be analyzed by the multi-parameter matrix constructed, not limited by parameter number and time sequence length, and by matrix similarity operation, judge the similarity of unit state, provide quick judgment basis for similar fault in later period;Dimensionality reduction is carried out to operation data matrix using matrix singular value decomposition method, retain the part of large singular value proportion, can extract fault feature, realize compressed storage, save storage space.

The invention provides an electromagnetic environment monitoring optimization point distribution method and system, and relates to the technical field of electromagnetic environment monitoring. In order to solve the problems that in the prior art, the representativeness and the accuracy of selected monitoring point positions are low, and the real environment of a region cannot be reflected, the method comprises the steps that a to-be-monitored region is divided into a plurality of grids, electromagnetic radiation indexes of the grids are obtained, and an original data matrix is constructed; determining a high-optimal index value according to an optimal index method, and performing normalization processing on the original data matrix to obtain a decision matrix; converting the decision matrix to obtain a proximity degree matrix; calculating similarity values among the grids based on the proximity degree matrix, and constructing a similar matrix; classifying the grids based on the similar matrix to obtain multiple groups of classification results; and for each group of classification results, analyzing the consistency of the same type of grids and the difference of different types of grids, determining a final classification result, and determining a final representative grid according to the final classification result. According to the method, the representativeness and the accuracy of selecting the final representative grid are relatively high.

This invention proposes a method and system for ECG clustering based on multi-lead semantic consistency, belonging to the field of ECG signal processing technology. The method includes: constructing a similarity correlation matrix for an ECG signal set using adaptive graph learning; learning the spectral representation of each lead of the ECG signal using spectral clustering on the similarity correlation matrix, whereby spectral clustering utilizes multi-lead shared spectral embedding to measure the correlation between ECG signals, obtained through an optimized minimum edge weight objective function; wherein the optimized minimum edge weight objective function includes the multi-lead shared semantic similarity matrix; after obtaining the spectral embedding matrix, constructing a spectral rotation objective function by introducing an orthogonal rotation factor matrix; merging the minimum edge weight objective function and the spectral rotation objective function into a total objective function, and optimizing the total objective function using the alternating direction multiplier method to obtain the ECG clustering result. This invention significantly improves the clustering quality through the learning of semantically consistent graphs.

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.

The invention discloses a terminal area approach mode mining method based on ASM-HAC. The method comprises the following steps: acquiring ADS-B data of an airport terminal area; the track features are screened, and the correlation degree between the track features is analyzed in combination with a Pearson's correlation coefficient; obtaining a track distance matrix by using a multi-dimensional dynamic time warping method under weighted Euclidean distance; improving a Gaussian kernel function by using adaptive parameters to obtain an ASM track similar matrix; a Laplacian matrix and a feature gap method are introduced to improve an HAC hierarchical clustering algorithm, and after the improved HAC hierarchical clustering algorithm is applied to an ASM track similar matrix, an optimal track clustering result is obtained; and evaluating the rationality of the optimal clustering number by adopting a contour coefficient. According to the method, the problem that an original hierarchical clustering method excessively depends on manual intervention to obtain the optimal clustering number is solved, the similarity measurement requirement of a track dense region and a track sparse region is balanced, the clustering process better fits the space-time heterogeneity of track distribution, a controller is assisted in mastering the characteristics of different approach modes, and control decision making is facilitated to be completed.

Apparatus (100, 120, 300, 304, 320, 330) and method (200, 350) for reducing the dimensionality of embeddings in a machine learning (ML) system. In some embodiments, original embeddings (124, 206, 378, 394, 404) from a pre-trained deep learning model (204, 322, 370, 390) are extracted for a set of data (202, 302), the original embeddings having an initial dimensionality. A set of projection vectors (128) are initialized (208, 352) with a specified dimensionality smaller than the initial dimensionality. A neural network (122, 304, 324, 340) is used to optimize the projection vectors by minimizing a loss function based on a similarity matrix (132, 134). The set of original embeddings are thereafter projected (210, 214, 360) onto the optimized projection vectors to obtain a set of reduced-dimensional embeddings (126, 216, 306, 388, 396, 406) with the specified dimensionality. A neural network of an ML system (308, 380, 392, 408) is thereafter configured using the reduced-dimensional embeddings, such as by a training operation (364) to duplicate operation of the deep learning model in a smaller latent space. The original embeddings may be single modal or multimodal embeddings.

The invention discloses an Internet of Things multi-view feature selection method and system based on similar matrix fusion, and relates to the technical field of machine learning, and the method comprises the steps: firstly splicing incomplete data of each view into a wide table according to columns, and constructing a complete matrix of each view through sampling and filling; secondly, clustering each view data for multiple times, and constructing a corresponding similar matrix; stacking all similar matrixes into tensors along a third dimension, introducing adaptive weight fusion under low-rank constraint, learning a unified common similar matrix to perform eigenvalue decomposition, and extracting eigenvectors corresponding to positive eigenvalues to form pseudo tags; in combination with a pace learning strategy, high-confidence samples are screened, and feature selection coefficients and self-paced weights of all views are jointly optimized; and calculating an importance score based on each view feature to obtain an optimal subset. Through similar matrix tensor fusion, adaptive low-rank constraint and pace learning joint optimization, feature selection of incomplete multi-view data of the industrial Internet of Things is realized.

A data processing method includes determining a first similarity matrix based on a query matrix and a key matrix corresponding to a word vector sequence; filtering out a target element whose value meets a condition from each row of the first similarity matrix, and setting all elements other than the target element in each row to zero to obtain a second similarity matrix; normalizing the second similarity matrix to obtain an attention weight matrix; and determining a self-attention matrix corresponding to the word vector sequence based on the attention weight matrix and a value matrix corresponding o the word vector sequence.

The application discloses a time series polarimetric SAR cumulative change detection method based on a similarity matrix, relates to the technical field of remote sensing image change detection, and comprises the following steps: a similarity matrix of all time phase polarimetric SAR images in a time series is constructed; the similarity matrix is linearly transformed, and the maximum eigenvalue of the similarity matrix is calculated; a difference map of cumulative change of the polarimetric SAR images in the time series is calculated according to the maximum eigenvalue; and the cumulative change detection result of the polarimetric SAR images in the time series is obtained by segmenting the difference map. The time series polarimetric SAR cumulative change detection method based on the similarity matrix provided by the application does not repeatedly calculate a difference map, and compared with a traditional detection method of continuously detecting two changes in a time series, the detection precision and operation efficiency are improved.

The invention belongs to the field of point cloud registration, and discloses a point cloud registration method and system, and the method comprises the steps: extracting plane points, carrying out the clustering of the plane points according to the normal vector of each plane point, dividing the planes in a plane group, and distinguishing different parallel plane layers in the same direction; normal vector included angles between different plane groups are calculated, the plane groups forming similar included angles are searched, rotation matrixes are calculated, similar matrixes are combined, planes in the groups are associated, and the planes of the corresponding plane groups correspond to one another; and calculating an optimal translation vector for each candidate rotation matrix and each plane corresponding condition, evaluating the quality of each calculated rotation matrix and translation vector, and selecting an optimal solution. According to the method, automatic and accurate extraction of the parallel plane group is realized under the condition that the plane direction does not need to be manually stipulated; registration is constrained through a plane geometrical relationship, similar planes are effectively distinguished, mismatching caused by too high geometrical similarity is avoided, and correct matching and alignment of parallel plane groups are achieved.

The invention discloses a bearing fault diagnosis method based on a cross-modal manifold density topological space, solves the problems that abnormal values are sensitive and non-linear modes of different density regions are difficult to capture under the condition of high dimension and small sample size, and effectively improves the accuracy of fault diagnosis. The specific implementation process is as follows: (1) constructing a relative neighborhood order module through Euclidean space, effectively identifying outliers with an asymmetric neighborhood relationship, constructing a cross-modal robust similar matrix in combination with a shared neighbor topological structure and local density distribution, and reducing the influence of noise and outliers; (2) constructing an objective function of a cross-modal manifold density topology model by adopting a divide-and-conquer strategy and considering local distribution characteristics of the data, and performing theoretical derivation on the objective function to obtain an analytical solution in a cross-modal manifold density topology projection direction; and (3) directly obtaining cross-modal manifold density topology fault features with good discrimination from the randomly selected bearing fault test sample through the cross-modal manifold density topology projection direction, and inputting the fault features into the classifier to obtain a final bearing fault diagnosis result. Compared with the prior art, the bearing fault diagnosis method is more accurate and robust.

The application discloses a multi-label feature selection method based on sparse learning coupled mutual information, and comprises the following steps: inputting a feature matrix X, a label matrix Y and hyperparameters alpha, beta, gamma and delta, selecting a feature number k, and initializing a label correlation matrix Z and a feature correlation matrix W; calculating a diagonal matrix A and a similarity matrix S according to the feature matrix X, and calculating a graph Laplacian similarity matrix L of the feature matrix X x ; updating the label correlation matrix Z and the feature correlation matrix W through a target function, iterating n times, and obtaining an updated label correlation matrix Z n and an updated feature correlation matrix W n after the target function reaches a convergence condition; and obtaining the selected k features according to the 2-norm of W n . The application adopts the multi-label feature selection method based on sparse learning coupled mutual information, and the effectiveness of the method is proved by experiments; and the method simultaneously solves the suboptimal solution problem caused by a random initialization strategy in the sparse learning method.

The application discloses an answer selection method considering the space-time dependent relationship of questions and answers, and the steps include: 1. constructing the question and answer data and performing data preprocessing; 2. using a BERT model to obtain a word-level similarity matrix of the question and answer pair; 3. splicing the similarity matrices of multiple answers under the same question thread to obtain a space-time tensor of the question and answer pair; and 4. using a ConvLSTM model to predict the matching degree of the question and answer pair. The application uses the BERT to obtain the word-level similarity matrix of the question and answer pair with implicit semantic correlation, constructs the space-time tensor of the question and answer pair based on the word-level similarity matrix, and learns the space-time dependent relationship information in the question and answer data through the ConvLSTM model, so that the selection of the answer to the question is finally realized, and thus the best answer with the highest matching degree to the question can be accurately recommended.

The application discloses a spoon defect detection method and system, electronic equipment and computer storage medium, and relates to the field of machine learning. The method comprises the following steps: acquiring a spoon image; constructing a spatial self-representation tensor according to the spoon image; constructing a multi-view clustering model based on rank approximation and sparse constraint according to the spatial self-representation tensor; solving a target function of the multi-view clustering model to obtain a similarity matrix of multi-view data; and performing clustering by using a spectral clustering algorithm according to the similarity matrix to obtain a spoon defect detection result. The application can improve clustering performance.

The application provides an electromagnetic environment monitoring optimization distribution method and system, and relates to the technical field of electromagnetic environment monitoring. In view of the low representativeness and precision of the selected monitoring points in the prior art, and the problem that the real environment of the region cannot be reflected, the application divides a region to be monitored into multiple grids, obtains electromagnetic radiation indexes of the grids, and constructs an original data matrix; the high optimal index value is determined according to the optimal index method, the original data matrix is normalized to obtain a decision matrix; the decision matrix is converted to obtain a closeness matrix; the similarity values between the grids are calculated based on the closeness matrix to construct a similarity matrix; the grids are classified based on the similarity matrix to obtain multiple sets of classification results; for each set of classification results, the consistency of the same type of grids and the difference of different types of grids are analyzed to determine a final classification result, and the final representative grid is determined according to the final classification result. The representativeness and precision of the selected final representative grid are high.

The invention discloses a microstrip transmission line dielectric constant measurement method based on similar matrix eigenvalue extraction. The method comprises the steps that firstly, a straight-through piece and a time delay piece are manufactured, and S parameters of the straight-through piece and the time delay piece are tested and converted into T parameter matrixes Tt and T1; and by constructing a similar matrix Ttl = TtT1, extracting a characteristic value of the similar matrix, and further obtaining a phase constant of the microstrip line. The phase constant and the dielectric constant have a definite mathematical relationship. And then, iteratively solving the dielectric constant by using a nonlinear least square method, and obtaining the dielectric constant of the measured medium when the two norm of the error between the calculated value and the theoretical value is smaller than a set threshold value. According to the invention, only two test fixtures are needed, rapid and accurate measurement of the dielectric constant is realized through the matrix eigenvalue extraction and optimization algorithm, and an effective solution is provided for electromagnetic parameter test of materials such as circuit boards and the like.

The invention discloses a passive cross-domain adaptive image classification method based on a multi-modal pre-training model, and relates to the technical field of transfer learning, and the method comprises the steps: firstly, carrying out the quantitative estimation of a class similar matrix of target domain data on a source model; then, introducing a category similar matrix of the source model into a CLIP (multi-modal pre-training model), directionally generating confusion category text features for classification, and obtaining a corresponding classification result; weighting the classification result of the source model and the classification result of the CLIP, and respectively aligning the output of the two models with the weighting result; calculating a category feature center library of each model according to the category similarity matrix; and finally, the attention network is used to align the projections of the input image on the two feature center libraries so as to realize the alignment of the feature space. According to the method, the similarity degree between the classes in the downstream tasks is dynamically estimated from the perspective of the models, learning is carried out by utilizing complementary information between the models, fine-grained classification between the similar classes is realized, and certain interpretability is achieved.

The invention discloses a synchronous phasor measurement unit data quality improvement method and device and a medium, and belongs to the technical field of power systems, and the method comprises the steps: obtaining single-channel data of a synchronous phasor measurement unit, and carrying out the segmentation processing of the single-channel data, and obtaining a plurality of one-dimensional data segments; quantifying distance values among the plurality of one-dimensional data segments, calculating distance values between each data segment and other data segments, and sorting according to the distance values to obtain a two-dimensional similar matrix; performing two-dimensional discrete cosine transform on the two-dimensional similar matrix to obtain a corresponding frequency domain coefficient matrix; performing adaptive hard threshold filtering on the frequency domain coefficient matrix to obtain a filtered frequency domain coefficient matrix; and performing weighted reconstruction on the filtered frequency domain coefficient matrix to obtain synchronous phasor measurement unit data after data quality improvement, thereby effectively realizing quality improvement of the PMU measurement data.

The application provides a point cloud registration method for a low overlap rate, relates to the technical field of three-dimensional point cloud registration, and comprises the following steps: collecting source point cloud and target point cloud; performing feature extraction and downsampling on the source point cloud and the target point cloud; performing position coding on the downsampled point cloud in a point cloud neighborhood range; adding the position coding result to the features; combining an attention module and optimal transport theory to improve the probability that homonymic point pairs of the point cloud are located in an overlapping part of the point cloud, and to improve point cloud registration precision; based on the optimal transport of a similarity matrix, missing corresponding points can be screened out from key points, and a conversion matrix can be directly solved through a credible point pair set constructed by a score mechanism and a spatial compatibility principle, so that the time spent on point cloud registration is shortened.

The invention discloses an underwater hyperspectral clustering method based on bidirectional square attenuation tensor regularization, and relates to the technical field of underwater image processing. Comprising the following steps: reconstructing multispectral data in a feature dimension and a sample dimension; performing singular value decomposition on the tensor of the feature projection matrix and the tensor of the sample similar matrix, and constructing square attenuation tensors of the feature projection matrix and the sample similar matrix based on singular values; introducing a Laplacian diagram into the sample similar matrix, and constructing a hyper-Laplacian regularization constraint; self-adaptive weight is introduced to balance the effect of each view sample; and solving the target function to obtain a comprehensive similarity matrix, and performing spectral clustering on the comprehensive similarity matrix to obtain a clustering result. According to the method, singular values can be contracted adaptively, important spectral features are reserved to the maximum extent, noise and redundant spectral information are strongly suppressed, the separability of different ground feature categories in a feature space is enhanced, and robustness is achieved for interference factors such as illumination changes and sensor noise.

The invention discloses a power consumer industry classification method and device, terminal equipment and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: obtaining a historical load curve set, and extracting a load curve time sequence in the historical load curve set; performing sub-fragment transformation according to pre-selected sub-fragments and the load curve time sequence to obtain a feature matrix; based on the feature matrix, taking a sample similar matrix and a Laplacian matrix as pseudo-class labels, taking a sub-fragment similar matrix as a sub-fragment diversity constraint, and constructing a target function of an unsupervised learning model; solving a target function, and outputting a trained sub-fragment set and a linear pseudo-label classifier; and obtaining a to-be-processed load curve, and outputting a user industry classification result by adopting the trained sub-fragment set and the linear pseudo-label classifier. The accuracy of power consumer industry classification can be effectively improved.

The present application belongs to the technical field of structural health monitoring, and discloses a method and equipment for recognizing compressive stress / damage based on transfer learning admittance features, which comprises the following steps: (1) constructing a structural health monitoring system of a piezoelectric sensing system based on piezoelectric admittance technology, and then collecting original admittance data of the structure under different compressive stress states within a predetermined frequency range; (2) enhancing the original admittance data based on Python language; (3) dividing part of the enhanced admittance data into multiple sub-frequency bands, calculating the RMSD value of each sub-frequency band, performing matrix multiplication on the RMSD value to obtain a similarity matrix, and then remodeling the similarity matrix into a two-dimensional data form; (4) inputting the similarity matrix in the form of two-dimensional data into an RB-DANN network model, and then the RB-DANN network model realizes data transfer between two domains and simultaneously realizes compressive stress / damage recognition; wherein the RB-DANN network model is constructed based on residual principle and field adaptation. The present application reduces the cost.

The application discloses an Internet of Things multi-view feature selection method and system based on similarity matrix fusion, and relates to the technical field of machine learning.The method comprises the following steps: firstly, incomplete data of each view is spliced into a wide table according to columns, and a complete matrix of each view is constructed through sampling and padding; secondly, the data of each view is clustered multiple times, and corresponding similarity matrices are constructed; all the similarity matrices are stacked into a tensor along the third dimension, an adaptive weight fusion is introduced under a low-rank constraint, a unified common similarity matrix is learned for feature value decomposition, a feature vector corresponding to a positive feature value is extracted to form a pseudo label; a high-confidence sample is screened in combination with a pace learning strategy, and each view feature selection coefficient and a self-pace weight are jointly optimized; and importance scores are calculated based on the features of each view to obtain an optimal subset.The application realizes feature selection of incomplete multi-view data of an industrial Internet of Things through similarity matrix tensor fusion, adaptive low-rank constraint and joint optimization of pace learning.

The invention relates to the technical field of data analysis in image processing. The invention provides a multi-view face data clustering method based on low-dimensional kernel domain difference measure, and the method comprises the steps: carrying out the noise reduction and redundancy elimination processing of face feature data of each view in multiple views, and generating a low-noise low-redundancy feature matrix after the dimension reduction; on the basis of the low-dimensional feature matrix, similarity measurement normal form reconstruction and optimization processing between the features are carried out, and an optimized distance measurement matrix is generated; according to the optimized distance measurement matrix, sample similarity learning is carried out, and an initial similarity matrix is generated; according to the method, the initial similarity matrix of each view angle is subjected to weighted fusion to form a consistency similar matrix, and finally, the consistency similar matrix is utilized to perform spectral clustering to generate a clustering result, so that the balance between the calculation efficiency and the clustering precision is improved, the adaptability of high-redundancy data is enhanced, and compared with other methods, the accuracy of the method is higher.