Clustering Variable Length Sequences by Eigenvector Decomposition Using HMM

@inproceedings{Porikli2004ClusteringVL,
  title={Clustering Variable Length Sequences by Eigenvector Decomposition Using HMM},
  author={Fatih Murat Porikli},
  booktitle={SSPR/SPR},
  year={2004}
}
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… CONTINUE READING
Highly Cited
This paper has 31 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

References

Publications referenced by this paper.
Showing 1-10 of 11 references

Computing interior eigenvalues of large matrices

  • R. B. Morgan
  • Linear Algebra Appl.,
  • 1991
Highly Influential
4 Excerpts

Pavlovic, ”Discovering clusters in motion timeseries data

  • J. Alon, S. Sclaroff, V G.Kollios
  • Proceedings of Computer Vision and Pattern…
  • 2003
1 Excerpt

Longuet-Higgins, “Feature grouping by relocalisation of eigenvectors of the proxmity matrix

  • H.C.G.L. Scott
  • In Proc. British Machine Vision Conference,
  • 1990
1 Excerpt

Similar Papers

Loading similar papers…