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 variable length sequences for various scenarios.


Hide Markov Model Gaussian Mixture Model Cluster Number Validity Score Output Symbol 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Fatih Porikli
    • 1
  1. 1.Mitsubishi Electric Research LaboratoriesCambridgeUSA

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