Feature Selection in the Tensor Product Feature Space

Classifying objects that are sampled jointly from two or more domains has many applications. The tensor product feature space is useful for modeling interactions between feature sets in different domains but feature selection in the tensor product feature space is challenging. Conventional feature selection methods ignore the structure of the feature space and may not provide the optimal results. In this paper we propose methods for selecting features in the original feature spaces of different domains. We obtained sparsity through two approaches, one using integer quadratic programming and another using L1-norm regularization. Experimental studies on biological data sets validate our approach.