A deep subspace clustering algorithm based on dual self-expression and the maximum entropy principle
The deep subspace clustering algorithm utilizes deep neural networks to map the original input data to a latent space and employs the self-expression of the data as a measure of data similarity,effectively achieving clustering of high-dimensional data.However,such algorithms only focus on the self-expressive relationship in the latent space,resulting in their performance heavily relying on the quality of features extracted by the deep neural networks.Additionally,the regularization process ig-nores the connectivity within each subspace,affecting the performance of spectral clustering.To address these issues,a deep subspace clustering algorithm based on dual self-expression and the maximum en-tropy principle is proposed.This algorithm simultaneously learns the self-expressive relationships in both the latent space and the input space,guiding the deep neural network to obtain data representations suitable for subspace clustering.By maximizing the entropy of the similarity matrix,it ensures that ele-ments within the same subspace are uniformly and densely distributed,thereby improving the perform-ance of data clustering.Extensive experiments on five datasets verify the effectiveness of the proposed algorithm.
subspace clusteringself-expressiondeep neural networkthe maximum entropy princi-ple
万方数据
维普
