Robust semi-supervised non-negative matrix factorization for binary subspace learning

Xiangguang Dai,Keke Zhang,Juntang Li,Jiang Xiong,Nian Zhang,Huaqing Li

doi:10.1007/s40747-021-00285-1

Abstract

Non-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization method for binary subspace learning, called RSNMF, for image clustering. For better clustering performance on the dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into non-negative matrix factorization. Moreover, we utilize the discrete hashing learning method to constrain the learned subspace, which can achieve a binary subspace from the original data. Experimental results validate the robustness and effectiveness of RSNMF in binary subspace learning and image clustering on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.

Highlights

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Robust Semi-Supervised Non-negative Matrix Factorization for Binary Subspace Learning”
Several dimensionality reduction techniques were presented such as principal components analysis (PCA) [4] and non-negative matrix factorization (NMF) [5], which can learn an effective subspace for classification
After dimensionality reduction by non-negative matrix factorization, the parts-based representation composed of real numbers will take much more time in clustering

Summary

Introduction

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Robust Semi-Supervised Non-negative Matrix Factorization for Binary Subspace Learning”. Several dimensionality reduction techniques were presented such as principal components analysis (PCA) [4] and non-negative matrix factorization (NMF) [5], which can learn an effective subspace for classification. When the original data are heavily corrupted, NMF fails to achieve clustering This is because its loss function is more sensitive to outliers and noise. After dimensionality reduction by non-negative matrix factorization, the parts-based representation composed of real numbers will take much more time in clustering. In this paper, based on data-dependent hashing methods, non-negative matrix factorization, and manifold learning, a novel dimensionality reduction method is presented to learn a subspace composed of binary codes from the original data space. The clustering performance from the subspace can demonstrate that our method can achieve the better clustering effect than other dimensionality reduction methods

Related works

Experimental results

Conclusion