Abstract
Multi-view feature fusion should be expected to mine implicit nature relationships among multiple views and effectively combine the data presented by multiple views to obtain the new feature representation of the object using a right model. In practical applications, Collective Matrix Factorization (CMF) has good effects on the fusion of multi-view data, but for noise-containing situations, the generalization ability is poor. Based on this, the paper came up with a Robust Collective Non-negative Matrix Factorization (RCNMF) model which can learn the shared feature representation of multi-view data and denoise at the same time. Based on several public data sets, experimental results fully demonstrate the effectiveness of the proposed method.
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