Learning with Structured Sparsity
The Journal of Machine Learning Research
A multi-stage framework for Dantzig selector and LASSO
The Journal of Machine Learning Research
Efficient ant colony optimization for image feature selection
Signal Processing
Compressed sensing signal recovery via forward-backward pursuit
Digital Signal Processing
Semi-supervised learning with manifold fitted graphs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-label learning under feature extraction budgets
Pattern Recognition Letters
Fast inference in generalized linear models via expected log-likelihoods
Journal of Computational Neuroscience
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Given a large number of basis functions that can be potentially more than the number of samples, we consider the problem of learning a sparse target function that can be expressed as a linear combination of a small number of these basis functions. We are interested in two closely related themes: · feature selection, or identifying the basis functions with nonzero coefficients; · estimation accuracy, or reconstructing the target function from noisy observations. Two heuristics that are widely used in practice are forward and backward greedy algorithms. First, we show that neither idea is adequate. Second, we propose a novel combination that is based on the forward greedy algorithm but takes backward steps adaptively whenever beneficial. For least squares regression, we develop strong theoretical results for the new procedure showing that it can effectively solve this problem under some assumptions. Experimental results support our theory.