Scalable greedy algorithms for transfer learning

We study the binary transfer learning leveraging on auxiliary source classifiers.We propose two efficient algorithms which select relevant sources from a large pool.One of the algorithms has computational cost independent from the number of sources.Our algorithms achieve state-of-the-art results on three computer vision datasets.We theoretically prove that our algorithm can learn effectively from few examples. In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the source data, but rather we employ the source hypotheses trained from them. We propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously, building on the literature on the best subset selection problem. Our algorithm achieves state-of-the-art results on three computer vision datasets, substantially outperforming both transfer learning and popular feature selection baselines in a small-sample setting. We also present a randomized variant that achieves the same results with the computational cost independent from the number of source hypotheses and feature dimensions. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples.