Feature Selection with Annealing for Big Data Learning
Adrian Barbu, Yiyuan She, Liangjing Ding, Gary Gramajo · arXiv (Cornell University) · 2013
Abstract—Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observa-tions and features. In this paper we propose a novel efficient learning scheme which tightens a sparsity constraint by gradually removing variables based on a criterion and a schedule. The attractive fact that the problem size keeps dropping throughout the iterations makes it particu-larly suitable for big data learning. Our approach applies generically to the optimization of any differentiable loss function, and finds applications in regression, classification and ranking. The resultant algorithms build variable screening into estimation and are extremely simple to imple-ment. We provide theoretical guarantees of convergence and selection consistency. In addition, one dimensional piecewise linear response functions are used to account for nonlinearity and a second order prior is imposed on these functions to avoid overfitting. Experiments on real and synthetic data show that the proposed method compares very well with other state of the art methods in regression, classification and ranking while being computationally very efficient and scalable. Index Terms—feature selection, supervised learning, regression, clas-sification, ranking. 1