In this paper, we propose a new feature selection method that can efficiently select representative features in the kernel space to improve the quality of linear models.
given a limited number of basic features such as the commonly used unigrams and bigrams, our method performs feature selection in the space of their combinations, e.g, the concatenation of these n-grams.