Storage Fit Learning with Unlabeled Data

By using abundant unlabeled data, semi-supervised learning approaches have been found useful in various tasks. Existing approaches, however, neglect the fact that the storage available for the learning process is different under different situations, and thus, the learning approaches should be flexible subject to the storage budget limit. In this paper, we focus on graph-based semi-supervised learning and propose two storage fit learning approaches which can adjust their behaviors to different storage budgets. Specifically, we utilize techniques of low-rank matrix approximation to find a low-rank approximator of the similarity matrix to meet the storage budget. The first approach is based on stochastic optimization, which is an iterative approach that converges to the optimal low-rank approximator globally. The second approach is based on Nyström method, which can find a good low-rank approximator efficiently and is suitable for real-time applications. Experiments show that the proposed methods can fit adaptively different storage budgets and obtain good performances in different scenarios.