Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering

TL;DR

This paper presents FSSMSC, a fast, scalable semi-supervised multi-view subspace clustering method that reduces computational complexity and improves clustering efficiency through a unified optimization framework and anchor graph approach.

Contribution

It introduces a novel unified optimization model for multi-view clustering that combines anchor graph construction and label propagation, enhancing scalability and efficiency.

Findings

Achieves linear computational complexity relative to data size.

Demonstrates superior clustering performance on benchmark datasets.

Provides convergence guarantees for the optimization algorithm.

Abstract

In this paper, we introduce a Fast and Scalable Semi-supervised Multi-view Subspace Clustering (FSSMSC) method, a novel solution to the high computational complexity commonly found in existing approaches. FSSMSC features linear computational and space complexity relative to the size of the data. The method generates a consensus anchor graph across all views, representing each data point as a sparse linear combination of chosen landmarks. Unlike traditional methods that manage the anchor graph construction and the label propagation process separately, this paper proposes a unified optimization model that facilitates simultaneous learning of both. An effective alternating update algorithm with convergence guarantees is proposed to solve the unified optimization model. Additionally, the method employs the obtained anchor graph and landmarks' low-dimensional representations to deduce low-dimensional representations for raw data. Following this, a straightforward clustering approach is conducted on these low-dimensional representations to achieve the final clustering results. The effectiveness and efficiency of FSSMSC are validated through extensive experiments on multiple benchmark datasets of varying scales.