Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence

作者：

Highlights：

• A new non-negative matrix factorization decomposition model is proposed.

• A new non-negative matrix factorization method, calledFeature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence (FNTNMF-KLD) is proposed.

• New iterative update rules for basis matrix and feature matrix are derived strictly.

• A proof of algorithm convergence is provided.

• There are higher accuracies in object recognition and clustering.

摘要

•A new non-negative matrix factorization decomposition model is proposed.•A new non-negative matrix factorization method, calledFeature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence (FNTNMF-KLD) is proposed.•New iterative update rules for basis matrix and feature matrix are derived strictly.•A proof of algorithm convergence is provided.•There are higher accuracies in object recognition and clustering.

论文关键词：Non-negative matrix factorization,Nonlinear transformation,Feature extraction,Object recognition,Clustering,Kullback-Leibler divergence

论文评审过程：Received 17 August 2020, Revised 6 August 2021, Accepted 14 July 2022, Available online 16 July 2022, Version of Record 30 July 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108906