A novel vector representation of stochastic signals based on adapted ergodic HMMs

Hao Tang, Mark Hasegawa-Johnson, Thomas Huang

Research output: Contribution to journalArticle

Abstract

In this letter, we propose a novel vector representation of stochastic signals for pattern recognition (PR) based on adapted ergodic hidden Markov models (HMMs). This vector representation is generic in nature and may be used with various types of stochastic signals (e.g., image, speech, etc.) and applied to a broad range of PR tasks (e.g., classification, regression, etc.). More importantly, by combining the vector representation with optimal distance metric learning (e.g., linear discriminant analysis) directly from the data, the performance of a PR system may be significantly improved. Our experiments on an image-based recognition task clearly demonstrate the effectiveness of the proposed vector representation of stochastic signals for potential use in many PR systems.

Original languageEnglish (US)
Article number5482148
Pages (from-to)715-718
Number of pages4
JournalIEEE Signal Processing Letters
Volume17
Issue number8
DOIs
StatePublished - Jul 1 2010

Keywords

  • Distance metric learning
  • hidden Markov model
  • pattern recognition
  • stochastic signal
  • vector representation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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