Sparse kernel partial least squares regression

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2777, Issue , 2003, Pages 216-230

  Momma, Michinari ^a   Bennett, Kristin P ^b  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b Research Rensselaer Polytechnic Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; ERROR ANALYSIS; ITERATIVE METHODS; PARAMETER ESTIMATION; PROBLEM SOLVING; REGRESSION ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS); LEAST SQUARES APPROXIMATIONS;

KERNEL PARTIAL LEAST SQUARE REGRESSION (KPLS); PARTIAL LEAST SQUARE REGRESSION (PLS); SPARSE SOLUTIONS; SUPPORT VECTORS; EPSILON-INSENSITIVE LOSS FUNCTION; KERNEL MATRICES; PARTIAL LEAST SQUARES REGRESSION; REGRESSION FUNCTION; SPARSE KERNELS; SUPPORT VECTOR REGRESSION (SVR); TUNING OF PARAMETERS;

INTEGER PROGRAMMING; REGRESSION ANALYSIS;

EID: 9444252818     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45167-9_17     Document Type: Conference Paper

References (11)

1. F. Bach and M. Jordan. Kernel independent component analysis. Journal of Machine Learning Research, 3:1-48, 2002.
2. K. Bennett and M. Embrechts. An optimization perspective on kernel partial least squares. In Proc. of NATO Adv. Study Inst. on Learning Theory and Prac., 2003.
3. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
4. A. Höskuldsson. PLS Regression Methods. Journ. of Chemometrics, 2:211-228, 1988.
5. J. Platt. Fast training of svm using sequential minimal optimization. In Adv, in Kernel Methods - Support Vector Learning. MIT Press, 1999.
6. R. Rosipal and L. Trejo. Kernel partial least squares regression in reproducing kernel Hilbert space. Journal of Machine Learning Research, 2:97-123, 201.
7. B. Schölkopf, A. Smola, and K.-R. Müller. Kernel principal component analysis. In Adv. in Kernel Methods - Support Vector Learning. MIT Press, 1999.
8. B. Schölkopf, A. Smola, and R. Williamson. Shrinking the tube: A new support regression algorithm. In Adv. in Neural Info. Proc. Systems 11. MIT Press, 1999.
9. V. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1996.
10. A. Vinokourov, J. Shawe-Taylor, and N. Cristianini. Finding language- independent semantic representation of text using kernel canonical correlation analysis. Neuro-COLT Technical Report NC-TR-02-119, 2002.
11. H. Wold. Estimation of principal components and related models by iterative least squares,. In Multivariate Analysis, New York, 1966. Academic Press.