Encoder-Decoder nonnegative matrix factorization with β-divergence for data clustering

doi:10.1016/j.patcog.2025.112211

Encoder-Decoder nonnegative matrix factorization with β-divergence for data clustering

2025-07-28

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AI

A

Amjad Seyedi

F

Fardin Akhlaghian Tab

F

Fatemeh Daneshfar

DOI：10.1016/j.patcog.2025.112211

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Abstract

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• β-divergence Encoder-Decoder NMF with graph regularization is proposed. • Self-representation β-divergence model refines the factorization process. • A unified optimization method solves the proposed objective function. • Experiments show effectiveness in clustering various data types.

Keywords:

β-divergence

Encoder-Decoder NMF

Graph regularization

Self-representation

Clustering

Encoder-Decoder nonnegative matrix factorization with β-divergence for data clustering

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