报 告 人：华南理工大学 谭明奎教授

报告时间：2017年2月13日上午9:00

报告地点：219会议室

报告题目：

MPGL: An Efficient Matching Pursuit Method for Generalized LASSO

报告人简介：

谭明奎，现年33岁，湖北巴东人，分别在2006年和2009年于湖南大学获得环境工程学士和模式识别与智能系统硕士学位，在2014年10月南洋理工大学获得计算机科学博士学位。自2014年6月到2016年9月，在阿德莱德大学沈春华教授组从事博士后研究，并于今年9月回国工作，获聘为华南理工大学教授/博导，目前主要研究方向为机器学习和机器视觉。截止今年11月，发表一作期刊论文6篇, 包括Journal of Machine Learning Research(1篇,CCF-A)，IEEE Trans 4篇；一作顶级会议论文9篇，包括ICML(2篇，CCF-A)，CVPR(2篇，CCF-A)，AAAI(3篇，CCF-A)，IJCAI(1篇，CCF-A)，CIKM(1篇，CCF-B)。截止2016年11月，被SCI他人引用160次，h指数12。

报告内容简介：

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 only involving 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.