@paper{AAAI1714429,
	author = {Dong Gong and Mingkui Tan and Yanning Zhang and Anton van den Hengel and Qinfeng Shi},
	title = {MPGL: An Efficient Matching Pursuit Method for Generalized LASSO},
	conference = {AAAI Conference on Artificial Intelligence},
	year = {2017},
	keywords = {generalized lasso; fused lasso; matching pursuit; convex programming; sparsity},
	abstract = {Unlike traditional LASSO enforcing sparsity on the variables, Generalized LASSO (GL) enforces sparsity on a linear transformation of the variables, gaining flexibility and success in many applications. However, many existing GL algorithms do not scale up to high-dimensional problems, and/or only work well for a specific choice of the transformation. We propose an efficient Matching Pursuit Generalized LASSO (MPGL) method, which overcomes these issues, and is guaranteed to converge to a global optimum. We formulate the GL problem as a convex quadratic constrained linear programming (QCLP) problem and tailor-make a cutting plane method. More specifically, our MPGL iteratively activates a subset of nonzero elements of the transformed variables, and solves a subproblem involving only the activated elements thus gaining significant speed-up. Moreover, MPGL is less sensitive to the choice of the trade-off hyper-parameter between data fitting and regularization, and mitigates the long-standing hyper-parameter tuning issue in many existing methods. Experiments demonstrate the superior efficiency and accuracy of the proposed method over the state-of-the-arts in both classification and image processing tasks.},

	url = {https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14429}
}