Masked Subspace Clustering Methods

TL;DR

This paper introduces a unified framework for masked subspace clustering that leverages learnable masks and bilevel optimization to enhance clustering performance on various datasets.

Contribution

It proposes a general Bilevel Clustering Optimization framework and three specialized masked subspace clustering methods, including a learnable soft mask approach.

Findings

Significant performance improvements over baselines on multiple datasets.

Effective integration of pairwise information through masks.

Demonstrated robustness across diverse clustering tasks.

Abstract

To further utilize the unsupervised features and pairwise information, we propose a general Bilevel Clustering Optimization (BCO) framework to improve the performance of clustering. And then we introduce three special cases on subspace clustering with two different types of masks. At first, we reformulate the original subspace clustering as a Basic Masked Subspace Clustering (BMSC), which reformulate the diagonal constraints to a hard mask. Then, we provide a General Masked Subspace Clustering (GMSC) method to integrate different clustering via a soft mask. Furthermore, based on BCO and GMSC, we induce a learnable soft mask and design a Recursive Masked Subspace Clustering (RMSC) method that can alternately update the affinity matrix and the soft mask. Numerical experiments show that our models obtain significant improvement compared with the baselines on several commonly used datasets, such as MNIST, USPS, ORL, COIL20 and COIL100.