Conference paper
Transformed anti-sparse learning for unsupervised hashing
Abstract
Anti-sparse representation was recently considered for unsupervised hashing, due to its remarkable robustness to the binary quantization error. We relax the existing spread property [4, 22] for anti-sparse solutions, to a new Relaxed Spread Property (RSP) that demands milder conditions. We then propose a novel Transformed Anti-Sparse Hashing (TASH) model to overcome several major bottlenecks, that have significantly limited the effectiveness of anti-sparse hashing models. TASH jointly learns a dimension-reduction transform, a dictionary and the anti-sparse representations in a unified formulation. We have conducted extensive experiments on real datasets and practical settings, and demonstrate the highly promising performance of TASH.
Related
Conference paper
Training Stronger Baselines for Learning to Optimize
Conference paper