Transformation-Invariant Collaborative Sub-representation

In this paper, we present an efficient and robust image representation method that can handle misalignment, occlusion and big noises with lower computational cost. It is motivated by the sub-selection technique, which uses partial observations to efficiently approximate the original high dimensional problems. While it is very efficient, their method can not handle many real problems in practical applications, such as misalignment, occlusion and big noises. To this end, we propose a robust sub-representation method, which can effectively handle these problems with an efficient scheme. While its performance guarantee was theoretically proved, numerous experiments on practical applications have further demonstrated that the proposed method can lead to significant performance improvement in terms of speed and accuracy.