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Partial Linearization Based Optimization for Multi-class SVM
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NETL
Springer Nature
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。
NETL
Springer Nature
