Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering

TL;DR

This paper introduces EMKCF, a scalable and memory-efficient method for large-scale multiple kernel clustering that improves clustering performance by effectively fusing multiple kernels.

Contribution

The paper proposes EMKCF, a novel approach that constructs sparse kernels and extends orthogonal concept factorization for efficient large-scale MKC.

Findings

EMKCF outperforms state-of-the-art methods on benchmark datasets.

EMKCF is scalable and memory-efficient for large datasets.

Experimental results validate the effectiveness of EMKCF.

Abstract

Kernel methods are extensively employed for nonlinear data clustering, yet their effectiveness heavily relies on selecting suitable kernels and associated parameters, posing challenges in advance determination. In response, Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information from multiple base kernels for clustering. However, both early fusion and late fusion methods for large-scale MKC encounter challenges in memory and time constraints, necessitating simultaneous optimization of both aspects. To address this issue, we propose Efficient Multiple Kernel Concept Factorization (EMKCF), which constructs a new sparse kernel matrix inspired by local regression to achieve memory efficiency. EMKCF learns consensus and individual representations by extending orthogonal concept factorization to handle multiple kernels for time efficiency. Experimental results demonstrate the efficiency and effectiveness of EMKCF on benchmark datasets compared to state-of-the-art methods. The proposed method offers a straightforward, scalable, and effective solution for large-scale MKC tasks.