Subspace Nonnegative Matrix Factorization for Feature Representation

TL;DR

This paper introduces a novel subspace-focused nonnegative matrix factorization method that adaptively weights features to improve representation accuracy, especially when some features are redundant or corrupted.

Contribution

It proposes two new weighted NMF strategies that focus on key features in a subspace, enhancing feature representation over traditional NMF.

Findings

More accurate feature representations on real-world datasets.

Effective identification of key features through adaptive weighting.

Simple iterative solutions for the proposed methods.

Abstract

Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, which means treating all features equally. However, a subspace is often sufficient for accurate representation in practical applications, and redundant features can be invalid or even harmful. For example, if a camera has some sensors destroyed, then the corresponding pixels in the photos from this camera are not helpful to identify the content, which means only the subspace consisting of remaining pixels is worthy of attention. This paper proposes a new NMF method by introducing adaptive weights to identify key features in the original space so that only a subspace involves generating the new representation. Two strategies are proposed to achieve this: the fuzzier weighted technique and entropy regularized weighted technique, both of which result in an iterative solution with a simple form. Experimental results on several real-world datasets demonstrated that the proposed methods can generate a more accurate feature representation than existing methods. The code developed in this study is available at https://github.com/WNMF1/FWNMF-ERWNMF.