Robust Multi-view Spectral Clustering with Auto-encoder for Preserving Information

Multi-view clustering is a prominent research topic in machine learning that leverages consistent and complementary information from multiple views to improve clustering performance. The graph-based multi-view clustering methods learn consistent graph with pairwise similarity between data as edges, and generate sample representation using spectral clustering. However, most existing methods seldom consider to recreate the input data using encoded representation during representation learning procedure, which result in information loss. To address this limitation, we propose a robust multi-view clustering with auto-encoder for preserving information (RMVSC-AE) that minimizes the reconstruction error between the input data and the reconstructed representation to preserve knowledge. Specifically, we discover a graph representation by jointly optimizing the graph Laplacian and auto-encoder reconstruction terms. Moreover, we introduce a sparse noisy term to further enhance the quality of the learned consistent graph. Extensive experiments on six multi-view datasets are conducted to verify the efficacy of the proposed method.