Presentation information

General Session

General Session » J-2 Machine learning

[4J2-GS-2] Machine learning: Clustering

Fri. Jun 12, 2020 12:00 PM - 1:20 PM Room J (jsai2020online-10)


12:20 PM - 12:40 PM

[4J2-GS-2-02] Performance Evaluation of GNMF-based Clustering Using Cluster Ensembles

〇Takehiro Sano1, Tsuyoshi Migita1, Norikazu Takahashi1 (1. Okayama University)

Keywords:clustering, graph regularized nonnegative matrix factorization, cluster ensembles

In recent years, Graph regularized Nonnegative Matrix Factorization (GNMF)-based clustering has attracted attention as a promising clustering method for large-scale high-dimensional data. GNMF was developed by applying the idea of manifold learning to NMF, and thus can conduct dimensionality reduction suitable for real-world data. However, minimizing the objective function for GNMF does not always result in a high clustering performance, because there is a gap between the objective function value and the clustering performance. In addition, there is a problem that the clustering results depend on the initial value, which has not been discussed in detail in many papers. In this paper, as a method to solve these problems, we propose a GNMF-based clustering using cluster ensembles. We also show experimentally that the proposed method stably achieves a high clustering performance.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.