Timezone: »

 
Poster
Subset Selection by Pareto Optimization
Chao Qian · Yang Yu · Zhi-Hua Zhou

Tue Dec 08 04:00 PM -- 08:59 PM (PST) @ 210 C #78
in Poster Session »

Selecting the optimal subset from a large set of variables is a fundamental problem in various learning tasks such as feature selection, sparse regression, dictionary learning, etc. In this paper, we propose the POSS approach which employs evolutionary Pareto optimization to find a small-sized subset with good performance. We prove that for sparse regression, POSS is able to achieve the best-so-far theoretically guaranteed approximation performance efficiently. Particularly, for the \emph{Exponential Decay} subclass, POSS is proven to achieve an optimal solution. Empirical study verifies the theoretical results, and exhibits the superior performance of POSS to greedy and convex relaxation methods.

[ PDF] 

Author Information

Chao Qian (Nanjing University)
Yang Yu (Nanjing University)
Zhi-Hua Zhou (Nanjing University)

More from the Same Authors