Sequence Classification Using Third-Order Moments

Rasmus Troelsgaard*, Lars Kai Hansen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.
Original languageEnglish
JournalNeural Computation
Issue number1
Pages (from-to)216-236
Publication statusPublished - 2017


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