Semisupervised learning of Hidden Markov models via a homotopy method

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 2, 2009, Pages 275-287

  Ji, Shihao ^a   Watson, Layne T ^b   Carin, Lawrence ^a  

^a Duke University   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Hidden Markov models (HMMs); Homotopy method; Semisupervised learning; Supervised learning

Indexed keywords

ALLOCATION PARAMETER; CLASSIFIER DESIGN; ESTIMATION PROBLEM; FIXED POINT EQUATION; HMM TRAINING; HOMOTOPY; HOMOTOPY METHOD; LABELED AND UNLABELED DATA; LABELED DATA; LOCAL OPTIMA; SEMI-SUPERVISED; SEMISUPERVISED LEARNING; SEQUENTIAL DATA; UNLABELED DATA;

COMPUTATIONAL GRAMMARS; EDUCATION; MAXIMUM LIKELIHOOD ESTIMATION; OBJECT RECOGNITION; SCADA SYSTEMS; SUPERVISED LEARNING; WAVELET TRANSFORMS;

HIDDEN MARKOV MODELS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; METHODOLOGY; PROBABILITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; MARKOV CHAINS; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 62249193672     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.71     Document Type: Article

References (20)

1. S.J. Raudys and A.K. Jain, "Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 252-264, Mar. 1991.
2. X. Zhu, Z. Ghahramani, and J. Lafferty, "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions," Proc. 20th Int'l Conf. Machine Learning, pp. 912-919, 2003.
3. M. Seeger, "Learning with Labeled and Unlabeled Data," technical report, Inst. for Adaptive and Neural Computation, Univ. of Edinburgh, 2001.
4. B. Shahshahani and D. Landgrebe, "The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon," IEEE Trans. Geoscience and Remote Sensing, vol. 32, 1994.
5. K. Nigam, A. Mccallum, S. Thrun, and T. Mitchell, "Text Classification from Labeled and Unlabeled Documents Using EM," Machine Learning, vol. 39, pp. 135-167, 2000.
6. F.G. Cozman, I. Cohen, and M.C. Cirelo, "Semi-Supervised Learning of Mixture Models," Proc. 20th Int'l Conf. Machine Learning, 2003.
7. M. Inoue and N. Ueda, "Exploitation of Unlabeled Sequences in Hidden Markov Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 12, Dec. 2003.
8. L.R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.
9. P. Runkle, P.K. Bharadwaj, L. Couchman, and L. Carin, "Hidden Markov Models for Multi-Aspect Target Classification," IEEE Trans. Signal Processing, vol. 47, pp. 2035-2040, July 1999.
10. E. Birney, "Hidden Markov Models in Biological Sequence Analysis," IBM J. Research and Development, vol. 45, 2001.
11. D. Cutting, J. Kupiec, J. Pedersen, and P. Sibun, "A Practical Part-of-Speech Tagger," Proc. Third Conf. Applied Natural Language Processing, pp. 133-140, 1992.
12. D.M. Bikel, R. Schwartz, and R.M. Weischedel, "An Algorithm that Learns What's in a Name," Machine Learning, vol. 34, pp. 211-231, Feb. 1999.
13. A. Dempster, N. Laird, and D. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc., vol. 39, pp. 1-38, 1977.
14. A. Corduneanu and T. Jaakkola, "Continuation Methods for Mixing Heterogeneous Sources," Proc. 18th Ann. Conf. Uncertainty in Artificial Intelligence, 2002.
15. S.N. Chow, J. Mallet-Paret, and J.A. Yorke, "Finding Zeros of Maps: Homotopy Methods that Are Constructive with Probability One," Math. of Computation, vol. 32, pp. 887-899, 1978.
16. L.T. Watson, M. Sosonkina, R.C. Melville, A.P. Morgan, and H.F. Walker, "Algorithm 777: HOMPACK90: A Suite of Fortran 90 Codes for Globally Convergent Homotopy Algorithms," ACM Trans. Math. Software, vol. 23, pp. 514-549, 1997.
17. E.L. Allgower and K. Georg, Numerical Continuation Methods: An Introduction. Springer, 1990.
18. T.M. Cover and J.A. Thomas, Elements of Information Theory. John Wiley & Sons, 1991.
19. S. Verdú and H.V. Poor, "On Minimax Robustness: A General Approach and Applications," IEEE Trans. Information Theory, vol. 30, no. 2, pp. 328-340, Mar. 1984.
20. R.M. Gray, "Vector Quantization," IEEE ASSP Magazine, pp. 4-29, Apr. 1984.