L1/2-regularized nonnegative matrix factorization for HMM-based sequence representation learning

Symbolic sequence representation plays a pivotal role in many resource-constrained expert systems. Recently, Hidden Markov Model (HMM)-based methods have received extensive interest due to their ability to capture underlying structural features with interpretability, especially for representation le...

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Bibliographic Details

Published in:	Expert systems with applications Vol. 300; p. 130378
Main Authors:	Cheng, Lingfang, Chen, Lifei, Zheng, Ping
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 05.03.2026
Subjects:	Between-state scatter Hidden Markov model L1/2-norm regularization Non-negative matrix factorization Sequence representation Hidden Markov model Between-state scatter Sequence representation Non-negative matrix factorization L1/2-norm regularization
ISSN:	0957-4174
Online Access:	Get full text
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