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In recent years, we have witnessed a surge of interest in multi-viewrepresentation learning, which is concerned with the problem of learningrepresentations of multi-view data. When facing multiple views that are highlyrelated but sightly different from each other, most of existing multi-viewmethods might fail to fully integrate multi-view information. Besides,correlations between features from multiple views always vary seriously, whichmakes multi-view representation challenging. Therefore, how to learnappropriate embedding from multi-view information is still an open problem butchallenging. To handle this issue, this paper proposes a novel multi-viewlearning method, named Multi-view Low-rank Preserving Embedding (MvLPE). Itintegrates different views into one centroid view by minimizing thedisagreement term, based on distance or similarity matrix among instances,between the centroid view and each view meanwhile maintaining low-rankreconstruction relations among samples for each view, which could make morefull use of compatible and complementary information from multi-view features.Unlike existing methods with additive parameters, the proposed method couldautomatically allocate a suitable weight for each view in multi-viewinformation fusion. However, MvLPE couldn't be directly solved, which makes theproposed MvLPE difficult to obtain an analytic solution. To this end, weapproximate this solution based on stationary hypothesis and normalizationpost-processing to efficiently obtain the optimal solution. Furthermore, aniterative alternating strategy is provided to solve this multi-viewrepresentation problem. The experiments on six benchmark datasets demonstratethat the proposed method outperforms its counterparts while achieving verycompetitive performance.

Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation