Multiple kernel concept factorization algorithm based on global fusion

TL;DR

This paper introduces GMKCF, a novel unsupervised clustering algorithm that combines multiple kernels through global fusion within the concept factorization framework, enhancing clustering quality and stability.

Contribution

It proposes a new multiple kernel concept factorization algorithm that automatically learns optimal kernel combinations, addressing kernel selection challenges in unsupervised clustering.

Findings

Outperforms existing clustering algorithms like KKM, SC, and KCF.

Demonstrates high clustering stability and quality across multiple datasets.

Proven convergence through iterative optimization.

Abstract

Non-negative Matrix Factorization(NMF) algorithm can only be used to find low rank approximation of original non-negative data while Concept Factorization(CF) algorithm extends matrix factorization to single non-linear kernel space, improving learning ability and adaptability of matrix factorization. In unsupervised environment, to design or select proper kernel function for specific dataset, a new algorithm called Globalized Multiple Kernel CF(GMKCF)was proposed. Multiple candidate kernel functions were input in the same time and learned in the CF framework based on global linear fusion, obtaining a clustering result with high quality and stability and solving the problem of kernel function selection that the CF faced. The convergence of the proposed algorithm was verified by solving the model with alternate iteration. The experimental results on several real databases show that the proposed algorithm outperforms comparison algorithms in data clustering, such as Kernel K-Means(KKM), Spectral Clustering(SC), Kernel CF(KCF), Co-regularized multi-view spectral clustering(Coreg), and Robust Multiple KKM(RMKKM).