TY - JOUR
T1 - A novel vector representation of stochastic signals based on adapted ergodic HMMs
AU - Tang, Hao
AU - Hasegawa-Johnson, Mark
AU - Huang, Thomas
N1 - Funding Information:
Manuscript received February 01, 2010; revised May 10, 2010; accepted May 14, 2010. Date of publication June 07, 2010; date of current version June 21, 2010. This work was supported in part by NSF 08-03219. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Patrizio Campisi.
Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Distance metric learning
KW - hidden Markov model
KW - pattern recognition
KW - stochastic signal
KW - vector representation
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U2 - 10.1109/LSP.2010.2051945
DO - 10.1109/LSP.2010.2051945
M3 - Article
AN - SCOPUS:77953988140
SN - 1070-9908
VL - 17
SP - 715
EP - 718
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 8
M1 - 5482148
ER -