Sparse Transfer Classification for Text Documents

Abstract

Transfer learning supports classification in domains varying from the learning domain. Prominent applications can be found in Wifi-localization, sentiment classification or robotics. A recent study shows that approximation of training trough test environments is leading to proper performance and out-dates the strategy most transfer learning approaches pursue. Additionally, sparse transfer learning models are required to address technical limitations and the demand for interpretability due to recent privacy regulations. In this work, we propose a new transfer learning approach which approximates the learning environment, combine it with the sparse and interpretable probabilistic classification vector machine and compare our solution with standard benchmarks in the field.