.png){kind=icon}
.png){kind=icon}
Return
Lp Norm Localized Multiple Kernel Learning via Semi-Definite Programming
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2012.2212431.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Our objective is to train SVM based Localized Multiple Kernel Learning with arbitrary l(p)-norm constraint using the alternating optimization between the standard SVM solvers with the localized combination of base kernels and associated sample-specific kernel weights. Unfortunately, the latter forms a difficult l(p)-norm constraint quadratic optimization. In this letter, by approximating the l(p)-norm using Taylor expansion, the problem of updating the localized kernel weights is reformulated as a non-convex quadratically constraint quadratic programming, and then solved via associated convex Semi-Definite Programming relaxation. Experiments on ten benchmark machine learning datasets demonstrate the advantages of our approach.
Keywords:
Localized multiple kernel learning
semi-definite programming
support vector machine
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W