Fast likelihood search for hidden Markov models

ACM Transactions on Knowledge Discovery from Data

Volumn 3, Issue 4, 2009, Pages

  Fujiwara, Yasuhiro ^a   Sakurai, Yasushi ^b   Kitsuregawa, Masaru ^c  

^a NTT CORPORATION   (Japan)

^b NTT Communication Science Laboratories   (Japan)

^c INSTITUTE OF INDUSTRIAL SCIENCE   (Japan)

Author keywords

Hidden Markov model; Likelihood; Upper bound

Indexed keywords

ANOMALY DETECTION; BEST MODEL; BIOLOGICAL ANALYSIS; DATA SETS; EFFICIENT MONITORING; FAST SEARCH METHOD; LIKELIHOOD; LIKELIHOOD COMPUTATION; MENTAL TASK CLASSIFICATION; QUERY SEQUENCE; STATE SEQUENCES; STREAMING DATA; TRAFFIC MONITORING; UPPER BOUND;

FEATURE EXTRACTION; OBJECT RECOGNITION;

HIDDEN MARKOV MODELS;

EID: 72449122835     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1631162.1631166     Document Type: Article

References (59)

1. ABADI, D. J.,CARNEY, D., ÇETINTEMEL, U., CHERNIACK, M., CONVEY, C., LEE, S., STONEBRAKER, M., TATBUL, N., AND ZDONIK, S. B. 2003. Aurora: a new model and architecture for data stream management. VLDB J. 12, 2, 120-139.
2. AGRAWAL, R., FALOUTSOS, C., AND SWAMI, A. N. 1993. Efficient similarity search in sequence databases. In Proceedings of the International Conference on Foundations of Data Organization and Algorithms (FODO). Lecture Notes in Computer Science, vol.730, Springer-Verlag, Berlin, Germany, 69-84.
3. AGRAWAL, R., LIN, K.-I., SAWHNEY, H. S., AND SHIM, K. 1995. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of the Conference on Very Large Databases (VLDB). ACM, New York, 490-501.
4. ARASU, A., BABCOCK, B., BABU, S., MCALISTER, J., AND WIDOM, J. 2002. Characterizing memory requirements for queries over continuous data streams. In Proceedings of the 21st ACMSIGMODSIGACT-SIGANT Symposium on Principles of Database Systems. ACM, New York, 221-232.
5. BABCOCK, B., BABU, S., DATAR, M., AND MOTWANI, R. 2003. Chain: Operator scheduling for memory minimization in data stream systems. In Proceedings of the SIGMOD Conference. ACM, New York, 253-264.
6. BALDI, P., CHAUVIN, Y., HUNKAPILLER, T., AND MCCLURE, M. A. 1994. Hidden Markov models of biological primary sequence information. Proc. Nat. Acad. Sci. 91, 1059-1063.
7. BARBARA, D., COUTO, J., JAJODIA, S., AND WU, N. 2001. Adam: A testbed for exploring the use of data mining in intrusion detection. SIGMOD Record 30, 4, 15-24.
8. BICKEL, P., CHEN, C., KWON, J., PRAVIN, J. R., AND ZWET, V. E. V. 2001. Traffic flow on a freeway network. In Proceedings of the Workshop on Nonlinear Estimation and Classification.
9. BISHOP, C. M. 2007. Pattern Recognition and Machine Learning. Springer-Verlag, Berlin, Germany.
10. BOCCHIERI, E. 1993. Vector quantization for the efficient computation of continuous density likelihoods. In Proceedings of the IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP). IEEE Computer Society Press, Los Alamitos, CA, 692-695.
11. CHANDRASEKARAN, S., COOPER, O., DESHPANDE, A., FRANKLIN, M. J., HELLERSTEIN, J. M., HONG, W., KRISHNAMURTHY, S.,MADDEN, S., RAMAN, V., REISS, F., AND SHAH,M. A. 2003. Telegraphcq: Continuous dataflow processing for an uncertain world. In Proceedings of the Conference on Innovative Data Systems Research (CIDR). ACM, New York.
12. CHENG, R., KALASHNIKOV, D. V., AND PRABHAKAR, S. 2003. Evaluating probabilistic queries over imprecise data. In Proceedings of the SIGMOD Conference. ACM, New York, 551-562.
13. CHENG, R., XIA, Y., PRABHAKAR, S., SHAH, R., AND VITTER, J. S. 2004. Efficient indexing methods for probabilistic threshold queries over uncertain data. In Proceedings of the Conference on Very Large Databases (VLDB). ACM, New York, 876-887.
14. CRANOR, C. D., JOHNSON, T., SPATSCHECK, O., AND SHKAPENYUK, V. 2003. Gigascope: A stream database for network applications. In Proceedings of the SIGMOD Conference. ACM, New York, 647-651.
15. DAS, G., GUNOPULOS, D., AND MANNILA, H. 1997. Finding similar time series. In Proceedings of the Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). 88-100.
16. DENNING,D.E. 1998. Cyberspace Attacks and Countermeasures.ACMPress/ Addison-Wesley Publishing Co., New York.
17. DESHPANDE, A., GUESTRIN, C., HONG, W., AND MADDEN, S. 2005. Exploiting correlated attributes in acquisitional query processing. In ICDE. 143-154.
18. DURBIN, R., EDDY, S. R., KROGH, A., AND MITCHISON, G. 1999. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
19. EICKELER, S., KOSMALA, A., AND RIGOLL, G. 1998. Hidden Markov model based continuous online gesture recognition. In ICPR. 1206-1208.
20. ESPOSITO, R. AND RADICIONI, D. P. 2007. Carpediem: An algorithm for the fast evaluation of SSL classifiers. In ICML. 257-264.
21. FALOUTSOS, C., RANGANATHAN, M., AND MANOLOPOULOS, Y. 1994. Fast subsequence matching in time-series databases. In Proceedings of the SIGMOD Conference. ACM, New York, 419-429.
22. FUJIWARA, Y., SAKURAI, Y., AND YAMAMURO,M. 2008. Spiral: Efficient and exact model identification for hidden Markov models. In KDD. 247-255.
23. GALES, M., KNILL, K., AND YOUNG, S. 1999. State-based Gaussian selection in large vocabulary continuous speech recognition using HMMS. In TSAP. 152-161.
24. GANTI, V., GEHRKE, J., AND RAMAKRISHNAN, R. 2000. Demon: Mining and monitoring evolving data. In ICDE. 439-448.
25. GAO, L. AND WANG, X. S. 2005. Continuous similarity-based queries on streaming time series. IEEE Trans. Knowl. Data Eng. 17, 10, 1320-1332.
26. GEHRKE, J., KORN, F., AND SRIVASTAVA, D. 2001. On computing correlated aggregates over continual data streams. In Proceedings of the SIGMOD Conference. ACM, New York, 13-24.
27. HAUSSLER, D., KROGH, A., MIAN, I. S., AND SJOLANDER, K. 1993. Protein modeling using hidden Markov models: Analysis of globins. In HICSS 39. 792-802.
28. HELBING, D., HERRMANN, H. J., SCHRECKENBERG, M., AND WOLF, D. E. 2000. Traffic and Granular Flow '99: Social, Traffic, and Granular Dynamics. Springer-Verlag, Berlin, Germany.
29. HU, J., BROWN, M. K., AND TURIN, W. 1996. HMM based on-line handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18, 10, 1039-1045.
30. HUANG, J., LIU, Z., ANDWANG, Y. 2005. Joint scene classification and segmentation based on hidden markov model. IEEE Trans. Multimed. 7, 3, 538-550. (Pubitemid 40795052)
31. HUNT, M. AND LEFEBVRE, C. 1989. A comparison of several acoustic representations for speech recognition with degraded and undegraded speech. In Proceedings of the IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP). IEEE Computer Society Press, Los Alamitos, CA, 262-265.
32. JELINEK, F. 1999. Statistical Methods for Speech Recognition. The MIT Press, Cambridge, MA.
33. KAHN, J. M., KATZ, R. H., AND PISTER, K. S. J. 1999. Next century challenges: Mobile networking for "smart dust". In Proceedings of MOBICOM. 271-278.
34. KAUFMAN, L. AND ROUSSEEUW, P. J. 2005. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience, New York.
35. KEOGH, E. J. 2002. Exact indexing of dynamic time warping. In Proceedings of the Conference on Very Large Databases (VLDB). ACM, New York, 406-417.
36. KWON, J. ANDMURPHY,K. 2000. Modeling freeway traffic with coupledhmms. Tech. Rep. University of California at Berkeley, Berkeley, CA.
37. LANE, T. 1999. Hidden markov models for human/computer interface modeling. In Proceedings of the IJCAI-99 Workshop on Learning About Users. 35-44.
38. LAW,M.H. C. ANDKWOK, J. T. 2000. Rival penalized competitive learning for model-based sequence clustering. In ICPR. 2195-2198.
39. LEVINSON, S. E., RABINER, L. R., AND SONDHI, M. M. 1982. An introduction to the application of the theory of probabilistic functions of a markov process to automatic speech recognition. Bell Syst. Tech. J. 62, 1035-1074.
40. LI, C. AND BISWAS, G. 1999. Clustering sequence data using hidden markov model representation. In Proceedings of the SPIE Conference on Data Mining and Knowledge Discovery: Theory, Tools, and Technology. 14-21.
41. LI, C. AND BISWAS,G. 2000. A Bayesian approach to temporal data clustering using hidden markov models. In ICML. 543-550.
42. MOON, Y.-S.,WHANG, K.-Y., ANDHAN,W.-S. 2002. General match: A subsequence matching method in time-series databases based on generalized windows. In Proceedings of the SIGMOD Conference. ACM, New York, 382-393.
43. MOTWANI, R., WIDOM, J., ARASU, A., BABCOCK, B., BABU, S., DATAR, M., MANKU, G. S., OLSTON, C., ROSENSTEIN, J., AND VARMA, R. 2003. Query processing, approximation, and resource management in a data stream management system. In Proceedings of the Conference on Innovative Data Systems Research (CIDR). ACM, New York.
44. MOUNT, D. W. 2001. Bioinformatics: Sequence and genome analysis. Cold Spring Harbor Laboratory Press.
45. NEY, H., MERGEL, D., NOLL, A., AND PAESLER, A. 1992. Data driven search organization for continuous speech recognition. IEEE Trans. Signal Processing. 40, 2, 272-281.
46. NOVAK, D., T. AL-ANI, Y. H., AND LHOTSKA, L. 2004. Electroencephalogram processing using hidden markov models. In EUROSIM.
47. PFURTSCHELLER, G., FLOTZINGER, D., AND NEUPER, C. 1994. Differentiation between finger, toe and tongue movement in man based on 40 hz EEG. Electroencephalog. Clin. Neurophysiol. 90, 6, 456-460.
48. RABINER, L. R. AND JUANG, B. H. 1986. An introduction to hidden markov models. IEEE ASSP Magazine 3, 4-16.
49. SAGAYAMA, S., KNILL, K., AND TAKAHASHI, S. 1995. On the use of scalar quantization for fast hmm computation. In Proceedings of the IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP). IEEE Computer Society Press, Los Alamitos, CA, 213-216.
50. SIDDIQI, S. M. AND MOORE, A. W. 2005. Fast inference and learning in large-state-space hmms. In ICML. 800-807.
51. SINGH, S. P., JAAKKOLA, T., AND JORDAN, M. I. 1994. Reinforcement learning with soft state aggregation. In NIPS. 361-368.
52. SMYTH, P. 1996. Clustering sequences with hidden markov models. In NIPS. 648-654.
53. TAO, Y., CHENG, R., XIAO, X., NGAI, W. K., KAO, B., AND PRABHAKAR, S. 2005. Indexing multidimensional uncertain data with arbitrary probability density functions. In Proceedings of the Conference on Very Large Databases (VLDB). ACM, New York, 922-933.
54. TATBUL, N., ÇETINTRNEL, U., ZDONIK, S. B., CHERNIACK, M., AND STONEBRAKER, M. 2003. Load shedding in a data stream manager. In Proceedings of the Conference on Very Large Databases (VLDB). ACM, New York, 309-320.
55. WARRENDER, C., FORREST, S., AND PEARLMUTTER, B. A. 1999. Detecting intrusions using system calls: Alternative data models. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society Press, Los Alamitos, CA, 133-145.
56. YI, B.-K., JAGADISH, H. V., AND FALOUTSOS, C. 1998. Efficient retrieval of similar time sequences under time warping. In Proceedings of the ICDE. 201-208.
57. ZHANG, T., RAMAKRISHNAN, R., AND LIVNY, M. 1996. Birch: An efficient data clustering method for very large databases. In Proceedings of the SIGMOD Conference. ACM, New York, 103-114.
58. ZHONG, S. AND GHOSH, J. 2002. Hmms and coupled hmms for multi-channel eeg classification. In Proceedings of the IEEE International Joint Conference on Neural Networks. IEEE Computer Society Press, Los Alamitos, CA, 1154-1159.
59. ZHU, Y. AND SHASHA, D. 2002. Statstream: Statistical monitoring of thousands of data streams in real time. In Proceedings of the Conference on Very Large Databases (VLDB). ACM, New York, 358-369.