Sparse Substring Pattern Set Discovery Using Linear Programming Boosting

In this paper, we consider finding a small set of substring patterns which classifies the given documents well. We formulate the problem as 1 norm soft margin optimization problem where each dimension corresponds to a substring pattern. Then we solve this problem by using LPBoost and an optimal substring discovery algorithm. Since the problem is a linear program, the resulting solution is likely to be sparse, which is useful for feature selection. We evaluate the proposed method for real data such as movie reviews.

• Type: Journal
• Year: 2010
• Where: DIS
• Authors: Kazuaki Kashihara, Kohei Hatano, Hideo Bannai, Masayuki Takeda
• Keywords: DIS 2010, Margin Optimization Problem, Optimal Substring Discovery, Substring Pattern, Theoretical Computer Science