Sequence classification via large margin hidden Markov models

Authors:
Minyoung Kim;Vladimir Pavlovic
Affiliations:
Department of Electronic & Information Engineering, Seoul National University of Science & Technology, Seoul, Korea 139-743;Department of Computer Science, Rutgers University, Piscataway, USA
Venue:
Data Mining and Knowledge Discovery
Year:
2011

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Abstract

We address the sequence classification problem using a probabilistic model based on hidden Markov models (HMMs). In contrast to commonly-used likelihood-based learning methods such as the joint/conditional maximum likelihood estimator, we introduce a discriminative learning algorithm that focuses on class margin maximization. Our approach has two main advantages: (i) As an extension of support vector machines (SVMs) to sequential, non-Euclidean data, the approach inherits benefits of margin-based classifiers, such as the provable generalization error bounds. (ii) Unlike many algorithms based on non-parametric estimation of similarity measures that enforce weak constraints on the data domain, our approach utilizes the HMM's latent Markov structure to regularize the model in the high-dimensional sequence space. We demonstrate significant improvements in classification performance of the proposed method in an extensive set of evaluations on time-series sequence data that frequently appear in data mining and computer vision domains.