Robust sequential data modeling using an outlier tolerant hidden markov model

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 9, 2009, Pages 1657-1669

  Chatzis, Sotirios P ^a   Kosmopoulos, Dimitrios I ^b   Varvarigou, Theodora A ^c  

^a UNIVERSITY OF MIAMI   (United States)

^b INSTITUTE OF NANOSCIENCE AND NANOTECHNOLOGY   (Greece)

^c NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Expectation maximization; Factor analysis; Hidden Markov models; Sequential data modeling; Student's t distribution

Indexed keywords

CONVENTIONAL APPROACH; COVARIANCE MATRICES; DATA SETS; EXPECTATION-MAXIMIZATION; FACTOR ANALYSIS; FINITE GAUSSIAN MIXTURE MODELS; FINITE MIXTURES; GAUSSIAN MIXTURE MODEL; HIDDEN STATE; MODEL PARAMETERS ESTIMATION; MULTIVARIATE STUDENT; SEQUENTIAL DATA; SEQUENTIAL DATA MODELING; T-MIXTURE MODELS;

BLIND SOURCE SEPARATION; COMMUNICATION CHANNELS (INFORMATION THEORY); COMPUTATIONAL GRAMMARS; COVARIANCE MATRIX; MAXIMUM LIKELIHOOD ESTIMATION; MAXIMUM PRINCIPLE; MIXTURES; OBJECT RECOGNITION; PARAMETER ESTIMATION; STUDENTS;

HIDDEN MARKOV MODELS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; INFORMATION RETRIEVAL; METHODOLOGY; PROBABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; INFORMATION STORAGE AND RETRIEVAL; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 67651005607     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.215     Document Type: Article

References (27)

1. O. Cappé, E. Moulines, and T. Rydén, Inference in Hidden Markov Models. 2005.
2. L. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, pp. 245-255, 1989.
3. A. Dempster, N. Laird, and D. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc. B vol. 39, no. 1, pp. 1-38, 1977.
4. D. Peel and G. McLachlan, "Robust Mixture Modeling Using the t Distribution," Statistics and Computing, vol. 10, pp. 335-344, 2000.
5. S. Chatzis, D. Kosmopoulos, and T. Varvarigou, "Signal Modeling and Classification Using a Robust Latent Space Model Based on t Distributions," IEEE Trans. Signal Processing, vol. 56, no. 3, pp. 949-963, 2008.
6. M. Svense'n and C.M. Bishop, "Robust Bayesian Mixture Modelling," Neurocomputing, vol. 64, pp. 235-252, 2005.
7. S. Chatzis and T. Varvarigou, "Factor Analysis Latent Subspace Modeling and Robust Fuzzy Clustering Using t Distributions," IEEE Trans. Fuzzy Systems, to appear.
8. L. Arslan and J. Hansen, "Selective Training for Hidden Markov Models with Applications to Speech Classification," IEEE Trans. Speech and Audio Processing, vol. 7, no. 1, pp. 46-54, 1999.
9. C.-H. Lee and S.-Y. Lee, "Noise-Robust Speech Recognition Using Top-Down Selective Attention with an HMM Classifier," IEEE Signal Processing Letters, vol. 14, no. 7, pp. 489-491, 2007.
10. J.-T. Chien and C.-P. Liao, "Maximum Confidence Hidden Markov Modeling for Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 4, pp. 606-616, Apr. 2008.
11. F. Sha and L.K. Saul, "Large Margin Hidden Markov Models for Automatic Speech Recognition," Advances in Neural Information Processing Systems, vol. 19, B. Scho"lkopf, J. Platt, and T. Hoffman, eds., pp. 1249-1256, MIT Press, 2007.
12. C. Liu and D. Rubin, "ML Estimation of the t Distribution Using EM and Its Extensions, ECM and ECME," Statistica Sinica, vol. 5, no. 1, pp. 19-39, 1995.
13. D. Andrews and C. Mallows, "Scale Mixtures of Normal Distributions," J. Royal Stat. Soc. B, vol. 36, pp. 99-102, 1974.
14. G. McLachlan, R. Bean, and L.B.-T. Jones, "Extension of the Mixture of Factor Analyzers Model to Incorporate the Multi- Variate t Distribution," Computational Statistics and Data Analysis, vol. 51, no. 11, pp. 5327-5338, 2006.
15. C. Archambeau and M. Verleysen, "Robust Bayesian Clustering," Neural Networks, vol. 20, pp. 129-138, 2007.
16. S. Dharanipragada and K. Visweswariah, "Gaussian Mixture Models with Covariances or Precisions in Shared Multiple Subspaces," IEEE Trans. Audio, Speech, and Language Processing, vol. 14, no. 4, pp. 1255-1266, 2006.
17. J. Lindblom and J. Samuelsson, "Bounded Support Gaussian Mixture Modeling of Speech Spectra," IEEE Trans. Speech and Audio Processing vol. 11, no. 1, pp. 88-99, 2003.
18. J. Frankel and S. King, "Speech Recognition Using Linear Dynamic Models," IEEE Trans. Audio, Speech, and Language Processing, vol. 15, no. 1, pp. 246-256, 2007.
19. M. Tipping and C. Bishop, "Mixtures of Probabilistic Principal Component Analyzers," Neural Computation, vol. 11, no. 2, pp. 443-482, 1999.
20. Z. Ghahramani and G. Hinton, "The EM Algorithm for Mixtures of Factor Analyzers," Technical Report CRGTR-96-1, Dept. of Computer Science, Univ. of Toronto, 1997.
21. X. Meng and D. van Dyk, "The EM Algorithm - An Old Folk Song Sung to a Fast New Tune (with Discussion)," J. Royal Statistical Soc. B, vol. 59, no. 3, pp. 511-567, 1997.
22. D. Kosmopoulos and I. Maglogiannis, "Hand Tracking for Gesture Recognition Tasks Using Dynamic Bayesian Network," Int'l J. Intelligent Systems and Applications, vol. 1, nos. 3/4, pp. 359-375, 2006.
23. R. Mukundan and K.R. Ramakrishnan, Moment Functions in Image Analysis: Theory and Applications. World Scientific, 1998.
24. L.F. Lamel, R.H. Kassel, and S. Seneff, "Speech Database Development: Design and Analysis of the Acoustic-Phonetic Corpus," Proc. DARPA Speech Recognition Workshop, pp. 100-109, 1986.
25. K.F. Lee and H.W. Hon, "Speaker-Independent Phone Recognition Using Hidden Markov Models," IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 37, no. 11, pp. 1641-1648, 1988.
26. T.M. Mitchell, R. Hutchinson, R.S. Niculescu, F. Pereira, X. Wang, M. Just, and S. Newman, "Learning to Decode Cognitive States from Brain Images," Machine Learning, vol. 57, no. 1, pp. 145-175, 2004.
27. G. McLachlan and D. Peel, Finite Mixture Models. Wiley, 2000.