A reproducing kernel Hilbert space framework for pairwise time series distances

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 624-631

  Lu, Zhengdong ^a   Leen, Todd K ^a   Huang, Yonghong ^a   Erdogmus, Deniz ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; HILBERT SPACES; LEARNING ALGORITHMS; MACHINE LEARNING; TIME SERIES; LEARNING SYSTEMS; ROBOT LEARNING;

CLASSIFICATION ACCURACY; DISCRETE OBSERVATIONS; DISTANCE MEASURE; GAUSSIAN PROCESSES; MIXED-EFFECT MODELS; REAL-WORLD PROBLEM; REPRODUCING KERNEL HILBERT SPACES; TEMPORAL STRUCTURES;

VECTOR SPACES; TIME SERIES;

CLASSIFICATION ACCURACIES; DISTANCE MEASURES; FEATURE VECTORS; GAUSSIAN PROCESSES; HILBERT; HILBERT SPACE FRAMEWORKS; MIXED-EFFECT MODELS; NON-PARAMETRIC; REAL-WORLD PROBLEMS; REPRODUCING KERNELS; SMOOTH CURVES; TEMPORAL STRUCTURES; UNEQUAL LENGTHS;

EID: 56449091021     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390235     Document Type: Conference Paper

References (21)

1. Altun, Y., Hofmann, T., & Smola, A. (2004). Exponential families for conditional random fields. Uncertainty in Artificial Intelligence(UAI).
2. Banerjee, A., Merugu, S., Dhillon, I. S., & Ghosh, J. (2005). Clustering with bregman divergences. JMLR, 6, 1705-1749.
3. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
4. Camicioli, R., Howieson, D., Oken, B., Sexton, G., & Kaye, J. (1998). Motor slowing precedes cognitive impairment in the oldest old. Neurology, 50.
5. Collins, M., Dasgupta, S., & Schapire, R. (2001). A generalization of principal component analysis to the exponential family. NIPS 13.
6. Forster, J., & Warmuth, M. K. (2000). Relative expected instantaneous loss bounds. COLT 13.
7. Frigyik, B., Srivastava, S., & Gupta, M. (2006). Functional bregman divergence and bayesian estimation of distributions. arXiv:cs/0611123.
8. Gelman, A. (2004). Bayesian data analysis, second edition. Chapman & Hall.
9. Green, M., Kaye, J., & Ball, M. (2000). The Oregon brain aging study: Neuropathology accompanying healthy aging in the oldest old. International Journal of Neural System, 54, 105-113.
10. Hayes, T., Pavel, M., & Kaye, J. (2004). An unobtrusive in-home monitoring system for detection of key motor changes preceding cognitive decline. 26th Annual International Conference of the IEEE EMBS.
11. Keogh, E., & Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. KDD 98 (pp. 239-241). ACM Press.
12. Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L., & Jordan, M. (2004). Learning the kernel with semidefinite Programming. Journal of Machine Learning Research, 5, 27-72.
13. Marquis, S., Moore, M., Howieson, D. B., Sexton, G., Payami, H., Kaye, J. A., & Camicioli, R. (2002). Indpendent predictors of cognitive decline in healthy elderly persons. Arch. Neurol., 59, 601-606.
14. Parra, L., Alvino, C., Tang, A., Pearlmutter, B., Yeung, N., Osman, A., & Sajda, P. (2003). Single trial detection in eeg and meg: Keeping it linear. Neurocomputing, 52-54, 177-183.
15. Ramsay, J., & Silverman, B. (1997). Functional data analysis. Springer-Verlag.
16. Ramsay, J. O., & Dalzell, C. J. (1991). Some tools for functional data analysis. Journal of the Royal Statistical Society, Series B (Methodological), 53, 539-572.
17. Rasmussen, C., & Williams, C. (2006). Gaussian processes for machine learning. Cambridge, MA: MIT Press.
18. Schwaighofer, A., Tresp, V., & Yu, K. (2005). Learning gaussian process kernels via hierarchical bayes. NIPS17.
19. Seeger, M. (2004). Gaussian process for machine learning. Internation Journal of Neural System, 14, 69-106.
20. Simon, B. (1979). Functional integration and quantum physics. Academic Press.
21. Tipping, M. (1999). Deriving cluster analytic distance functions from gaussian mixture models.