Eigenvector Decomposition using HMM Fatih Porikli Mitsubishi Electric...  Home/Search
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Abstract: We present a novel clustering method using HMM parameter space and eigenvector decomposition. Unlike the existing methods, our algorithm can cluster both constant and variable length sequences without requiring normalization of data. We show that the number of clusters governs the number of eigenvectors used to span the feature similarity space. We are thus able to automatically compute the optimal number of clusters. We successfully show that the proposed method accurately clusters... (Update)
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BibTeX entry: (Update)
@misc{ using-clustering,
author = "Eigenvector Decomposition Using",
title = "Clustering Variable Length Sequences by",
url = "citeseer.ist.psu.edu/767551.html" }
Citations (may not include all citations):1362 A tutorial on hidden markov models and selected applications.. (context) - Rabiner - 1989
193 Normalized cuts and image segmentation - Shi, Malik - 1997
144 A Jacobi-Davidson iteration method for linear eigenvalue pro.. - Sleijpen, Van Der Vorst - 1996
57 Segmentation using eigenvectors: a unifying view - Weiss - 1999
46 Clustering sequences with Hidden Markov Models - Smyth - 1997
35 Computing interior eigenvalues of large matrices (context) - Morgan - 1991
14 Feature grouping by relocalisation of eigenvectors of the pr.. (context) - Scott, Longuet-Higgins - 1990
6 Discovering clusters in motion timeseries data - Alon, Sclaro et al. - 2003
5 Dataclustering:areview (context) - Jain, Murty et al. - 1999
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