Learning with Heterogenous Data Sets by Weighted Multiple Kernel Canonical Correlation Analysis
- 1 August 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15512541,p. 81-86
Abstract
A new formulation of weighted multiple kernel based canonical correlation analysis(WMKCCA) is proposed in this paper. Computational issues are also considered in the proposed method to make it feasible on large data sets. This method uses incomplete Cholesky decomposition(ICD) and singular value decomposition(S VD) to approximate the original eigenvalue problem for low rank. For the weighted extension, an incremental eigenvalue decomposition method is proposed to avoid recalculating eigenvalue each time weights are changed. Based on WMKCCA we proposed, a machine learning framework to extract common information among heterogeneous data sets is purposed and experimental results on two UCI data sets are reported.Keywords
This publication has 7 references indexed in Scilit:
- Canonical Correlation Analysis: An Overview with Application to Learning MethodsNeural Computation, 2004
- Learning Eigenfunctions Links Spectral Embedding and Kernel PCANeural Computation, 2004
- Kernel independent component analysis2004
- Improving RBF-DDA performance on optical character recognition through parameter selection2004
- Application of semiparametric density estimation to classification2004
- Canonical Analysis of Several Sets of VariablesBiometrika, 1971
- RELATIONS BETWEEN TWO SETS OF VARIATESBiometrika, 1936