Symbolic sequence representation with Markovian state optimization
作者：
Highlights：
• The first effort on the HMM state optimization problem, i.e., optimizing the number of states and the discriminative quality of the states itself.
• New representation model for symbolic sequences using their transition probability distributions over the optimized HMM states called topics.
• Formalization of the hierarchical model selection problem for topic learning with a novel topic-scatter criterion.
• Learning the underlying topics by a newly defined HMM state clustering algorithm.
• Experimental evaluation on human activity recognition and protein recognition with comparisons to the neural network-based auto-encoder.
摘要
•The first effort on the HMM state optimization problem, i.e., optimizing the number of states and the discriminative quality of the states itself.•New representation model for symbolic sequences using their transition probability distributions over the optimized HMM states called topics.•Formalization of the hierarchical model selection problem for topic learning with a novel topic-scatter criterion.•Learning the underlying topics by a newly defined HMM state clustering algorithm.•Experimental evaluation on human activity recognition and protein recognition with comparisons to the neural network-based auto-encoder.
论文关键词：Sequence representation,Hidden Markov model,State clustering,Hierarchical model selection,Activity recognition
论文评审过程：Received 15 June 2021, Revised 15 April 2022, Accepted 12 June 2022, Available online 14 June 2022, Version of Record 18 June 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108849
