Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering

TL;DR

This paper introduces a novel multi-aspect data clustering method that incorporates inter-manifold information into Non-negative Matrix Factorization, improving clustering accuracy and efficiency across diverse datasets.

Contribution

It extends NMF-based manifold learning by integrating inter-manifold information, enabling better representation and clustering of multi-aspect data.

Findings

Outperforms state-of-the-art methods in accuracy

Achieves higher efficiency in clustering tasks

Effectively captures interrelated data types

Abstract

Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multi-dimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.