Non-Linear Canonical Correlation Analysis Using Alpha-Beta Divergence
AbstractWe propose a generalized method of the canonical correlation analysis using Alpha-Beta divergence, called AB-canonical analysis (ABCA). From observations of two random variables, x ∈ RP and y ∈ RQ, ABCA finds directions, wx ∈ RP and wy ∈ RQ, such that the AB-divergence between the joint distribution of (wT x, wT y) and the product x y of their marginal distributions is maximized. The number of significant non-zero canonical coefficients are determined by using a sequential permutation test. The advantage of our method over the standard canonical correlation analysis (CCA) is that it can reconstruct the hidden non-linear relationship between wT xx and wT y, and it is robust against outliers. We extend ABCA when data are observed in terms of tensors. We further generalize this method by imposing sparseness constraints. Extensive simulation study is performed to justify our approach. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Mandal, A.; Cichocki, A. Non-Linear Canonical Correlation Analysis Using Alpha-Beta Divergence. Entropy 2013, 15, 2788-2804.
Mandal A, Cichocki A. Non-Linear Canonical Correlation Analysis Using Alpha-Beta Divergence. Entropy. 2013; 15(7):2788-2804.Chicago/Turabian Style
Mandal, Abhijit; Cichocki, Andrzej. 2013. "Non-Linear Canonical Correlation Analysis Using Alpha-Beta Divergence." Entropy 15, no. 7: 2788-2804.