Weighted Multiple Kernel Canonical Correlation

[1]  Silvio Romero de Lemos Meira,et al.  Improving RBF-DDA performance on optical character recognition through parameter selection , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[2]  B. De Moor,et al.  Learning with Heterogenous Data Sets by Weighted Multiple Kernel Canonical Correlation Analysis , 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.

[3]  F. Hoti,et al.  Application of semiparametric density estimation to classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[4]  J. Kettenring,et al.  Canonical Analysis of Several Sets of Variables , 2022 .

[5]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[6]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

[7]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[8]  Bernhard Schölkopf,et al.  Kernel Methods for Measuring Independence , 2005, J. Mach. Learn. Res..

[9]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[10]  Hyun-Chul Kim,et al.  Support Vector Machine Ensemble with Bagging , 2002, SVM.

[11]  Shotaro Akaho,et al.  A kernel method for canonical correlation analysis , 2006, ArXiv.

[12]  Yuan-chin Ivan Chang,et al.  Data Visualization via Kernel Machines , 2008 .

[13]  Hyun-Chul Kim,et al.  Pattern classification using support vector machine ensemble , 2002, Object recognition supported by user interaction for service robots.