Low-Rank Kernel Learning with Bregman Matrix Divergences Brian Kulis, Matyas A. Sustik and Inderjit S. Dhillon Journal of Machine Learning Research 10 (2009) 341-376. Presented by: Peng Zhang 4/15/2011. Outline. Motivation Major Contributions Preliminaries Algorithms Discussions
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Low-Rank Kernel Learning with Bregman Matrix DivergencesBrian Kulis, Matyas A. Sustik and Inderjit S. DhillonJournal of Machine Learning Research 10 (2009) 341-376
Presented by:
Peng Zhang
4/15/2011
Low rank kernel matrix learning
Intuitively these can be thought of as the difference between the value of F at point x and the value of the first-order Taylor expansion of F around point y evaluated at point x.
All for full rank matrices
Range …
Rank …
O(cr^2)
Convergence is checked by how much v has changed
May require large number of iterations
O(nr^2)
Root finder, slows down the process
O(r^2)
Significant improvements
0.948 classification accuracy
For DefiniteBoost, 3220 cycles to convergence
Rank 57Rank 8
LogDet needs fewer constraints
LogDet converges much more slowly
(Future work)
But often it has fewer overall running time
Thank you