SLRL: Structured Latent Representation Learning for Multi-view Clustering

TL;DR

SLRL introduces a novel multi-view clustering framework that leverages both complementary and structural sample information via graph learning, significantly improving clustering performance on multiple datasets.

Contribution

The paper proposes SLRL, a new method that incorporates structural information among samples into multi-view clustering, addressing a key gap in existing approaches.

Findings

SLRL outperforms existing methods on multiple datasets.

It effectively exploits structural sample relationships.

The approach sets new benchmarks in multi-view clustering.

Abstract

In recent years, Multi-View Clustering (MVC) has attracted increasing attention for its potential to reduce the annotation burden associated with large datasets. The aim of MVC is to exploit the inherent consistency and complementarity among different views, thereby integrating information from multiple perspectives to improve clustering outcomes. Despite extensive research in MVC, most existing methods focus predominantly on harnessing complementary information across views to enhance clustering effectiveness, often neglecting the structural information among samples, which is crucial for exploring sample correlations. To address this gap, we introduce a novel framework, termed Structured Latent Representation Learning based Multi-View Clustering method (SLRL). SLRL leverages both the complementary and structural information. Initially, it learns a common latent representation for all views. Subsequently, to exploit the structural information among samples, a k-nearest neighbor graph is constructed from this common latent representation. This graph facilitates enhanced sample interaction through graph learning techniques, leading to a structured latent representation optimized for clustering. Extensive experiments demonstrate that SLRL not only competes well with existing methods but also sets new benchmarks in various multi-view datasets.