# Image Analysis Based on Nonnegative/Binary Matrix Factorization

Source: [Image Analysis Based on Nonnegative/Binary Matrix Factorization](<https://www.sota2.com/research/paper/68fa3c561e209131ce8d9a1e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T01:03:59.652Z

Authors: Hinako Asaoka, Kazue Kudo

Published: 2020-07-02T05:22:36.000Z

## Abstract

Using nonnegative/binary matrix factorization (NBMF), a matrix can be decomposed into a nonnegative matrix and a binary matrix. Our analysis of facial images, based on NBMF and using the Fujitsu Digital Annealer, leads to successful image reconstruction and image classification. The NBMF algorithm converges in fewer iterations than those required for the convergence of nonnegative matrix factorization (NMF), although both techniques perform comparably in image classification.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.00889>)

[PDF](<https://arxiv.org/pdf/2007.00889.pdf>)