The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction

TL;DR

This paper introduces a similarity-consensus based multi-view learning framework for dimension reduction, effectively leveraging correlations among heterogeneous features from multiple views to improve low-dimensional embeddings.

Contribution

It proposes a novel multi-view learning approach that preserves similarity across views, extending existing DR methods to handle multi-view data with scalability and robustness.

Findings

Achieves comparable performance on benchmark datasets.

Effectively captures correlations among multi-view features.

Demonstrates robustness and scalability in multi-view learning.

Abstract

During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it's not accessible for these DR methods to achieve satisfactory performance when facing the features from multiple views. In multi-view learning problems, one instance can be represented by multiple heterogeneous features, which are highly related but sometimes look different from each other. In addition, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view learning methods. Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging. To implement the above target, this paper proposes a novel multi-view learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering the scalability and robustness of the framework. It aims to straightforwardly extend those existing DR methods into multi-view learning domain by preserving the similarity between different views to capture the low-dimensional embedding. Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other and then an iterative alternating strategy is developed to obtain the optimal solution. The proposed method is evaluated on 5 benchmark datasets and comprehensive experiments show that our proposed multi-view framework can yield comparable and promising performance with previous approaches proposed in recent literatures.