Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization

TL;DR

This paper introduces a novel multi-modal multi-view non-negative matrix factorization method that enhances clustering by integrating multiple data modalities and views, demonstrating promising results across various datasets.

Contribution

The paper proposes a new multi-modal multi-view NMF approach that analyzes collaboration among local NMF models for improved clustering performance.

Findings

Outperforms existing methods on multiple datasets

Effective in multi-modal and multi-view data scenarios

Simplifies interpretation through non-negative matrices

Abstract

By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set. Non-negative Matrix Factorization (NMF) is a data mining technique that splits data matrices by imposing restrictions on the elements' non-negativity into two matrices: one representing the data partitions and the other to represent the cluster prototypes of the data set. This method has attracted a lot of attention and is used in a wide range of applications, including text mining, clustering, language modeling, music transcription, and neuroscience (gene separation). The interpretation of the generated matrices is made simpler by the absence of negative values. In this article, we propose a study on multi-modal clustering algorithms and present a novel method called multi-modal multi-view non-negative matrix factorization, in which we analyze the collaboration of several local NMF models. The experimental results show the value of the proposed approach, which was evaluated using a variety of data sets, and the obtained results are very promising compared to state of art methods.