7 results about "Centering matrix" patented technology

The application discloses an anchor point guided clustering based data classification method, device and equipment, relates to the technical field of digital data processing, and comprises the following steps: obtaining an original data set to be classified, converting each sample data into a numerical vector, and constructing a data matrix; initializing a clustering center matrix and an anchor point matrix, and setting a fuzzy coefficient; obtaining the anchor point matrix and the clustering center matrix after joint iterative optimization; calculating the fuzzy membership degree of each sample vector; the category with the maximum fuzzy membership degree is taken as the final category label of the sample vector, and the classification results of all sample vectors are output. The application solves the problems that the existing fuzzy clustering method is sensitive to initial conditions, is easy to fall into a suboptimal solution, leads to unstable and inaccurate classification results, and cannot be directly solved by using a gradient descent algorithm, and is difficult to be applied to large-scale data sets, and realizes the enhancement of complex data classification precision and the applicability in different data scale scenes.

The application provides a hyperspectral image band selection method, device and electronic equipment. The method comprises: sampling each band image of a hyperspectral image in a three-dimensional space to obtain a sampling two-dimensional matrix; calculating the information entropy of each sampling band in the sampling two-dimensional matrix, grouping the information entropy, selecting a sampling band corresponding to the maximum information entropy in each group to form an initial clustering center matrix; performing bias processing on the initial clustering center matrix to obtain a target clustering center matrix; based on the sampling two-dimensional matrix and the target clustering center matrix, calculating the membership degree of each sampling band in the sampling two-dimensional matrix to each clustering center in the target clustering center matrix to obtain an initial membership matrix; calculating a kernel matrix based on the sampling two-dimensional matrix; and determining a target band selected in the hyperspectral image based on the kernel matrix and the initial membership matrix. The method is efficient in calculation and improves the local optimal solution problem.

The invention discloses a data classification method, device and equipment based on anchor guide clustering, and relates to the technical field of electrical digital data processing, the method comprises the following steps: obtaining a to-be-classified original data set, converting each sample data into a numerical vector, and constructing a data matrix; initializing a clustering center matrix and an anchor point matrix, and setting a fuzzy coefficient; obtaining an anchor point matrix and a clustering center matrix after joint iterative optimization; calculating the fuzzy membership degree of each sample vector; the category with the maximum fuzzy membership degree value serves as a final category label of the sample vectors, and classification results of all the sample vectors are output. The problems that an existing fuzzy clustering method is sensitive to initial conditions and is prone to falling into a suboptimal solution, so that a classification result is unstable and inaccurate, and a gradient descent algorithm cannot be directly used for solving and is difficult to adapt to a large-scale data set are solved; the method achieves the enhancement of the classification precision of complex data and the applicability in different data scale scenes.

The application discloses a power distribution operation scene construction method and device based on load clustering, and the method comprises the following steps: acquiring multiple groups of historical load data; performing feature extraction on each group of historical load data respectively to obtain a load feature matrix; combining a preset initial cluster number to determine an initial cluster center set from the load feature matrix; performing clustering analysis based on each initial cluster center in the initial cluster center set to obtain a historical clustering center matrix; acquiring a synthetic clustering center matrix, fusing the historical clustering center matrix, and giving a mixed clustering center matrix; performing secondary clustering analysis based on the mixed clustering center matrix to obtain a target clustering center matrix, so as to complete the construction of the power distribution operation scene. Through clustering of historical load data of a power distribution network, load characteristic analysis is realized, a load feature matrix is obtained through feature extraction, and potential change rules are mined based on the load feature matrix, so that the target clustering center matrix is given, and the accuracy of the construction of the power distribution operation scene is improved.

The application provides a random subspace-based coastal zone ecological disaster mitigation assessment method, device, equipment and storage medium, a sample matrix is constructed, and a feature transformation matrix, a discrete coding matrix and a clustering center matrix are initialized, in an iterative updating process, a similarity matrix of each sample belonging to a feature transformation matrix array vector set is calculated, different types of coastal zone samples can be associated to different feature subspaces, and therefore the limitation of a single subspace assumption is broken through; finally, key risk assessment indexes are screened out according to L2 norm ordering of each row vector of the feature transformation matrix, and targeted ecological disaster mitigation risk assessment on a coastal zone region with spatial heterogeneity is realized.

The invention provides a spot day-ahead market decision sample matrix clustering preprocessing method and system, and belongs to the technical field of electricity market and data preprocessing, and the method comprises the steps: determining the time granularity and statistical period of a sample, and constructing a sample matrix; introducing a dimension weight matrix for each cluster, defining a cluster center matrix, updating a cluster center under a typical fuzzy clustering framework, and adaptively updating a dimension weight according to a weighted variance in the cluster; explicitly introducing a nuclear norm regular term into the clustering objective function; solving the minimum value of the clustering objective function by adopting an alternating iterative optimization strategy; and outputting the cluster to which each sample belongs, the membership of the cluster, the typical operation mode corresponding to each cluster and the abnormal sample set. According to the method, abnormal working condition samples such as extreme climate, sudden maintenance and data errors can be effectively identified, the robustness of a subsequent prediction and optimization model is improved, and the efficiency and precision of links such as day-ahead market quotation strategy optimization, flexibility evaluation and safety check are improved.

The application discloses an image processing model training method, an image processing method and an electronic device, relates to the technical field of image processing model training, and comprises the following steps: obtaining a current batch of samples in a training image set, removing the category labels of the samples to obtain a first category set, and extracting a first category center vector set from a target category center matrix; a second category set is formed by the remaining category labels in the training image set which do not belong to the first category set, a category is sampled from the second category set according to an importance weight, a second category center vector set is extracted, and the importance weight is determined according to the correlation degree between a category center vector and a sample feature vector and the gradient norm of the category center vector in a preset number of iterations; the first and second category center vector sets are merged, and the training parameters are updated according to the loss value calculated by the sample feature vector and the merged set, thereby solving the problems of large memory consumption and insufficient training of few sample categories during image processing model training, and improving the training reliability.