Deep Robust Encoder Through Locality Preserving Low-Rank Dictionary

Abstract

Deep learning has attracted increasing attentions recently due to its appealing performance in various tasks. As a principal way of deep feature learning, deep auto-encoder has been widely discussed in such problems as dimensionality reduction and model pre-training. Conventional auto-encoder and its variants usually involve additive noises (e.g., Gaussian, masking) for training data to learn robust features, which, however, did not consider the already corrupted data. In this paper, we propose a novel Deep Robust Encoder (DRE) through locality preserving low-rank dictionary to extract robust and discriminative features from corrupted data, where a low-rank dictionary and a regularized deep auto-encoder are jointly optimized. First, we propose a novel loss function in the output layer with a learned low-rank clean dictionary and corresponding weights with locality information, which ensures that the reconstruction is noise free. Second, discriminant graph regularizers that preserve the local geometric structure for the data are developed to guide the deep feature learning in each encoding layer. Experimental results on several benchmarks including object and face images verify the effectiveness of our algorithm by comparing with the state-of-the-art approaches.