A Convex Formulation for Spectral Shrunk Clustering

TL;DR

This paper introduces a convex spectral clustering algorithm that effectively mines manifold structures in low-dimensional spaces, leading to more accurate and structured clustering results, validated through extensive experiments.

Contribution

The paper presents a novel convex formulation for spectral clustering that directly learns manifold structures in the reduced-dimensional space, improving clustering quality.

Findings

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

Produces more structured and meaningful clusters

Demonstrates promising clustering performance in experiments

Abstract

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.