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Symbolic sequence representation with Markovian state optimization
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DOI：10.1016/j.patcog.2022.108849.png)
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Sequence representation, which is aimed at embedding sequentially symbolic data in a real space, is a foundational task in sequence pattern recognition. It is a difficult problem due to the challenges entailed in learning the intrinsic structural features within sequences in small sample size cases, in an unsuper-vised way. In this paper, we propose to represent each symbolic sequence by its transition probability distribution over discriminating topics, formalized by a set of optimized Hidden Markov Model (HMM) states shared by all sequences. An efficient method, called Markovian state clustering with hierarchical model selection, is proposed to optimize the Markovian states and to adaptively determine the number of topics. The proposed method is experimentally evaluated on human activity recognition and protein recognition, and results obtained demonstrate its effectiveness and efficiency.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Sequence representation
Hidden Markov model
State clustering
Hierarchical model selection
Activity recognition
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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