Hashing has attracted attention in recent years due to the rapid growth of image and video data on the web. Benefiting from recent advances in deep learning, deep supervised hashing has achieved promising results for image retrieval. However, existing methods are either less efficient in data usage or incapable of learning linearly discriminative binary codes. In this paper, we revisit linear discriminative analysis and propose a linear discriminative hashing (LDH) objective that is efficient in training and achieves better accuracy in retrieval. With the joint supervision of a classification loss, we design a robust deep network to obtain binary codes that are inter-class separable and intra-class compact, which provides better representations for image retrieval. We conduct extensive experiments on three benchmark datasets, and our LDH algorithm performs favorably against existing state-of-the-art deep supervised hashing methods.