Sparse multi-view image clustering with complete similarity information

Multi-view image clustering aims to efficiently divide the collection of images by studying the characteristics of different views. Many studies performed Laplacian dimensionality reduction on the original image to avoid noise interference in constructing the similarity matrix. However, they ignored the problem that local similarity information and isolated image information are lost due to dimensionality reduction. These problems will result in the similarity matrix being insufficient to accurately describe the similarity between images, which in turn affects the clustering accuracy. Therefore, we propose a sparse multi-view spectral clustering model with complete similarity information between images (SMSC-CSI). The model combines the original image space and the lowdimensional spectral embedding space based on the adaptive neighbors method to learn the initial similarity matrices of each view jointly. On the one hand, the original space can retain all the similarity information between images so that the initial similarity matrix can accurately describe the similarity between images, which is conducive to accurate clustering. On the other hand, the low-dimensional space can avoid noise interference and retain the main structure of high -dimensional data, improving the robustness of the initial similarity matrix. Meanwhile, to ensure consistency among the views, the model minimizes the difference between the initial similarity matrix and the central fusion matrix by alternately updating to obtain the optimal weights and central fusion matrix for each view. Finally, we can obtain ideal clustering results directly with low model complexity and without post -processing steps such as K-means by adding non -negative Laplace rank constraints and & ell; 0 -norm constraints to the model objective function. Experimental results on different real image datasets show that SMSC-CSI can outperform some traditional clustering models and recent models on multi-view image clustering.