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Published in:
2016 | OriginalPaper | Chapter
Partial Linearization Based Optimization for Multi-class SVM
Authors : Pritish Mohapatra, Puneet Kumar Dokania, C. V. Jawahar, M. Pawan Kumar
Published in: Computer Vision – ECCV 2016
Publisher: Springer International Publishing
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Abstract
We propose a novel partial linearization based approach for optimizing the multi-class svm learning problem. Our method is an intuitive generalization of the Frank-Wolfe and the exponentiated gradient algorithms. In particular, it allows us to combine several of their desirable qualities into one approach: (i) the use of an expectation oracle (which provides the marginals over each output class) in order to estimate an informative descent direction, similar to exponentiated gradient; (ii) analytical computation of the optimal step-size in the descent direction that guarantees an increase in the dual objective, similar to Frank-Wolfe; and (iii) a block coordinate formulation similar to the one proposed for Frank-Wolfe, which allows us to solve large-scale problems. Using the challenging computer vision problems of action classification, object recognition and gesture recognition, we demonstrate the efficacy of our approach on training multi-class svms with standard, publicly available, machine learning datasets.