Latent Multi-view Semi-Supervised Classification

TL;DR

This paper introduces LMSSC, a novel semi-supervised classification method that learns a latent representation from multiple views to improve graph accuracy and robustness, leading to better classification results.

Contribution

The paper proposes a unified framework that integrates latent representation learning, graph construction, and label propagation for multi-view semi-supervised classification.

Findings

Improves classification accuracy on benchmark datasets.

Learns more comprehensive data representations than single-view methods.

Enhances robustness and accuracy of graph-based semi-supervised learning.

Abstract

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.