Locality-Constrained Sparse Auto-Encoder for Image Classification

Abstract

We propose a locality-constrained sparse auto-encoder (LSAE) for image classification in this letter. Previous work has shown that the locality is more essential than sparsity for classification task. We here introduce the concept of locality into the auto-encoder, which enables the auto-encoder to encode similar inputs using similar features. The proposed LSAE can be trained by the existing backprop algorithm; no complicated optimization is involved. Experiments on the CIFAR-10, STL-10 and Caltech-101 datasets validate the effectiveness of LSAE for classification task.