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

TL;DR

This paper introduces MvLPE, a novel multi-view learning method that effectively integrates multiple related views into a unified representation by minimizing disagreement and preserving low-rank relations, outperforming existing methods.

Contribution

The paper proposes MvLPE, a new multi-view embedding approach that automatically weights views and maintains low-rank relations, addressing limitations of existing methods in multi-view data integration.

Findings

Outperforms existing multi-view methods on benchmark datasets.

Effectively integrates multiple views with automatic weighting.

Maintains low-rank relations for better representation quality.

Abstract

In recent years, we have witnessed a surge of interest in multi-view representation learning, which is concerned with the problem of learning representations of multi-view data. When facing multiple views that are highly related but sightly different from each other, most of existing multi-view methods might fail to fully integrate multi-view information. Besides, correlations between features from multiple views always vary seriously, which makes multi-view representation challenging. Therefore, how to learn appropriate embedding from multi-view information is still an open problem but challenging. To handle this issue, this paper proposes a novel multi-view learning method, named Multi-view Low-rank Preserving Embedding (MvLPE). It integrates different views into one centroid view by minimizing the disagreement term, based on distance or similarity matrix among instances, between the centroid view and each view meanwhile maintaining low-rank reconstruction relations among samples for each view, which could make more full use of compatible and complementary information from multi-view features. Unlike existing methods with additive parameters, the proposed method could automatically allocate a suitable weight for each view in multi-view information fusion. However, MvLPE couldn't be directly solved, which makes the proposed MvLPE difficult to obtain an analytic solution. To this end, we approximate this solution based on stationary hypothesis and normalization post-processing to efficiently obtain the optimal solution. Furthermore, an iterative alternating strategy is provided to solve this multi-view representation problem. The experiments on six benchmark datasets demonstrate that the proposed method outperforms its counterparts while achieving very competitive performance.