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Multi-view subspace clustering always performs well in high-dimensional dataanalysis, but is sensitive to the quality of data representation. To this end,a two stage fusion strategy is proposed to embed representation learning intothe process of multi-view subspace clustering. This paper first propose a novelmatrix factorization method that can separate the coupling consistent andcomplementary information from observations of multiple views. Based on theobtained latent representations, we further propose two subspace clusteringstrategies: feature-level fusion and subspace-level hierarchical strategy.Feature-level method concatenates all kinds of latent representations frommultiple views, and the original problem therefore degenerates to a single-viewsubspace clustering process. Subspace-level hierarchical method performsdifferent self-expressive reconstruction processes on the correspondingcomplementary and consistent latent representations coming from each view, i.e.the prior constraints imposed on different types of subspace representationsare related to the appropriate input factors. Finally, extensive experimentalresults on real-world datasets demonstrate the superiority of our proposedmethods by comparing against some state-of-the-art subspace clusteringalgorithms.

Partially latent factors based multi-view subspace learning