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Activity Number: 246 - Bayesian Nonparametrics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #301756
Title: Scalable Bayesian Nonlinear SVMs for Big Data Problems
Author(s): Sounak Chakraborty*
Companies: University of Missouri, Columbia
Keywords: SVM; Quasi-MCMC; Classification; Variable Selection ; spike and slab; scalability

In Big Data platforms, nonlinear SVMs are not very popular due to the difficulties in calculating and using the Gram/Kernel matrix. We employ a MCMC and Quasi-MCMC based solution to extract low dimensional random features and use them for approximating the Kernel matrix very efficiently and then use it in the model for faster and more accurate calculations. Our Bayesian SVM model is primarily for solving classification problems (binary and multiclass support vector machines). The feature selection is integrated in the framework Gaussian spike and slab priors. We propose a computationally scalable Gibbs sampling algorithm, which has linear computational complexity for covariate selections. Efficiency of our method for supervised and semi-supervised SVM models are demonstrated based on several simulation studies and data analysis.

Authors who are presenting talks have a * after their name.

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