Scalable Feedback of Spectral Projection for Subspace Learning

Abstract

Representation based methods for subspace learning consist of two stages: Affinity learning and spectral clustering. This letter proposes a feedback strategy to softly combine the two separate stages together by simultaneously optimizing the affinity and spectral projection. The soft feedback strategy can strengthen the required block-diagonal structure of the affinity matrix for most of the existing state-of-art algorithms. Using the feedback strategy, a scalable and projection unified model is given for datasets in large scale. A fast and efficient algorithm is also given to solve this problem, based on active piece-wise sign updating. Experiments are reported to demonstrate the improvement for the existing algorithms and the effectiveness and efficiency of the proposed model on large scale datasets.