Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering

TL;DR

This paper introduces a novel iterative framework for subspace clustering that learns a discriminative feature domain, improving clustering accuracy by integrating fuzzy sparse clustering with discriminative transformation learning.

Contribution

It proposes a new framework combining fuzzy sparse subspace clustering with discriminative transformation learning, enhancing discrimination and robustness in subspace clustering.

Findings

Achieves significant improvements in clustering accuracy.

Demonstrates effectiveness through theoretical analysis.

Validates superiority on benchmark datasets.

Abstract

This paper develops a novel iterative framework for subspace clustering in a learned discriminative feature domain. This framework consists of two modules of fuzzy sparse subspace clustering and discriminative transformation learning. In the first module, fuzzy latent labels containing discriminative information and latent representations capturing the subspace structure will be simultaneously evaluated in a feature domain. Then the linear transforming operator with respect to the feature domain will be successively updated in the second module with the advantages of more discrimination, subspace structure preservation and robustness to outliers. These two modules will be alternatively carried out and both theoretical analysis and empirical evaluations will demonstrate its effectiveness and superiorities. In particular, experimental results on three benchmark databases for subspace clustering clearly illustrate that the proposed framework can achieve significant improvements than other state-of-the-art approaches in terms of clustering accuracy.