Adaptive Feature Ranking for Unsupervised Transfer Learning

Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain. In this paper, we propose a method and efficient algorithm for ranking and selecting representa- tions from a Restricted Boltzmann Machine trained on a source domain to be transferred onto a target domain.