Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent

TL;DR

This paper introduces MVSC-HFD, a multi-view clustering method that uses hierarchical feature descent and anchor-based strategies to improve alignment, reduce computational complexity, and outperform existing techniques.

Contribution

The paper proposes a novel multi-view clustering framework that addresses feature alignment issues and reduces complexity to linear time using hierarchical feature descent and sampling strategies.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Achieves linear time complexity in clustering process

Effectively aligns features across multiple views

Abstract

Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up till now, many advanced approaches have been proposed in recent literature. However, there are several ongoing difficulties to be tackled. One common dilemma occurs while attempting to align the features of different views. {Moreover, due to the fact that many existing multi-view clustering algorithms stem from spectral clustering, this results to cubic time complexity w.r.t. the number of dataset. However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views. We further reduce the computational complexity to linear time cost through a unified sampling strategy in the common subspace( STAGE 2), followed by anchor-based subspace clustering to learn the bipartite graph collectively( STAGE 3). }Extensive experimental results on public benchmark datasets demonstrate that our proposed model consistently outperforms the state-of-the-art techniques.