Smoothed Multi-View Subspace Clustering

TL;DR

This paper introduces SMVSC, a novel multi-view clustering method that uses graph filtering to produce smooth, clustering-friendly representations, improving performance on benchmark datasets.

Contribution

The paper proposes a new multi-view clustering approach employing graph filtering to enhance data representation and clustering effectiveness.

Findings

Graph filtering increases class separability.

SMVSC outperforms existing methods on benchmark datasets.

Smooth representations facilitate downstream clustering tasks.

Abstract

In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views. However, multi-view data can be very complicated and are not easy to cluster in real-world applications. Most existing methods operate on raw data and may not obtain the optimal solution. In this work, we propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) by employing a novel technique, i.e., graph filtering, to obtain a smooth representation for each view, in which similar data points have similar feature values. Specifically, it retains the graph geometric features through applying a low-pass filter. Consequently, it produces a ``clustering-friendly" representation and greatly facilitates the downstream clustering task. Extensive experiments on benchmark datasets validate the superiority of our approach. Analysis shows that graph filtering increases the separability of classes.