A robust Hidden Markov Model based clustering algorithm

Hidden Markov models (HMMs) are widely employed in sequential data modeling both because they are capable of handling multivariate data of varying length, and because they capture the underlying hidden properties of time-series. Over the years, HMM-based clustering methods have been widely investiga...

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Bibliographic Details
Published in:2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference Vol. 2; pp. 259 - 264
Main Author: Shitong Yao
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2011
Subjects:
Accuracy
Algorithm design and analysis
Clustering algorithms
Computational modeling
Fisher Kernel
Hidden Markov models
HMM
Kernel
Noise
Spectral Clustering
ISBN:142448622X, 9781424486229
Online Access:Get full text
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Summary:Hidden Markov models (HMMs) are widely employed in sequential data modeling both because they are capable of handling multivariate data of varying length, and because they capture the underlying hidden properties of time-series. Over the years, HMM-based clustering methods have been widely investigated and improved. However, their performance on noisy data and the effectiveness of similarity measure between sequences remain less explored. In this paper, we present a robust algorithm for sequential data clustering by combining spectral analysis with HMMs. We first derive Fisher kernels from continuous density HMMs for similarity matrix construction, and then apply spectral clustering algorithm to the mapped data. The eigenvector decomposition step in spectral analysis is critical for noise removal and dimensionality reduction. Experimental results on both synthetic and real-world data indicate that our proposed approach is more tolerant to noise and achieves improved accuracy compared to many state-of-the-art algorithms.
ISBN:142448622X
9781424486229
DOI:10.1109/ITAIC.2011.6030325