On Covariate Shift Adaptation via Sparse Filtering

A major challenge in machine learning is covariate shift, i.e., the problem of training data and test data coming from different distributions. This paper studies the feasibility of tackling this problem by means of sparse filtering. We show that the sparse filtering algorithm intrinsically addresses this problem, but it has limited capacity for covariate shift adaptation. To overcome this limit, we propose a novel semi-supervised sparse filtering algorithm, named periodic sparse filtering. Our proposed algorithm is formally analyzed and empirically evaluated with an elaborated synthetic data set and real speech emotion data sets. As a result, we argue that, as an alternative methodology, feature distribution learning has enormous potential in carrying out covariate shift adaptation.