Dear all,
I would like to announce the following publication:
Suykens J.A.K., "SVD revisited: a new variational principle, compatible
feature maps and nonlinear extensions", Applied and Computational
Harmonic Analysis, 40 (2016), pp. 600-609. doi:10.1016/j.acha.2015.09.004
http://www.sciencedirect.com/science/article/pii/S1063520315001360
http://www.esat.kuleuven.be/stadius/ADB/publications.php
- it proposes a new variational principle for the matrix singular
value decomposition, in a kernel-based learning setting;
- it goes beyond Mercer kernels (which are commonly used in the
kernel trick);
- nonlinear extensions are shown to the matrix SVD
- kernel PCA corresponds to a special case of the formulations, related
to the case of a square symmetric matrix.
Best regards,
Johan Suykens
----------------------
Prof. Johan Suykens
Katholieke Universiteit Leuven
Departement Elektrotechniek - ESAT-STADIUS
Kasteelpark Arenberg 10
B-3001 Leuven (Heverlee)
Belgium
http://www.esat.kuleuven.be/stadius/members/suykens.html
http://www.esat.kuleuven.be/stadius/ADB
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