Multi-view Reconstructive Preserving Embedding for Dimension Reduction

TL;DR

This paper introduces MRPE, a novel multi-view dimension reduction technique that preserves neighborhood structures across multiple high-dimensional feature spaces, improving tasks like classification and recognition.

Contribution

The paper proposes MRPE, a new method that effectively integrates multiple views in nonlinear high-dimensional spaces for better dimension reduction.

Findings

MRPE outperforms existing methods in document classification.

MRPE improves face recognition accuracy.

MRPE enhances image retrieval performance.

Abstract

With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can re ect various perspectives of one same sample, so there must be compatible and complementary information among the multiple views. Therefore, it's natural to integrate multiple features together to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reduction method named Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE reconstructs each sample by utilizing its k nearest neighbors. The similarities between each sample and its neighbors are primely mapped into lower-dimensional space in order to preserve the underlying neighborhood structure of the original manifold. MRPE fully exploits correlations between each sample and its neighbors from multiple views by linear reconstruction. Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding. Various evaluations based on the applications of document classification, face recognition and image retrieval demonstrate the effectiveness of our proposed approach on multi-view dimension reduction.