Binary Orthogonal Non-negative Matrix Factorization

TL;DR

This paper introduces BONMF, a fast and space-efficient binary orthogonal non-negative matrix factorization technique that improves clustering and classification accuracy on real-world datasets.

Contribution

The paper presents a novel BONMF method that enhances accuracy and efficiency in clustering and classification tasks compared to existing techniques.

Findings

Improved accuracy over related techniques

Fast training and classification

Space-efficient implementation

Abstract

We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.