Symbolic sequence representation with Markovian state optimization. (November 2022) - Chen, Lifei; Wu, Haiyan; Kang, Wenxuan; Wang, Shengrui
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. Abstract: 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 unsupervised 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.