Locality Preserving Discriminative Hashing

Kang Zhao, Hongtao Lu, Yangcheng He, Shaokun Feng

Abstract: Hashing for large scale similarity search has become more and more popular because of its improvement in computational speed and storage reduction. Semi-supervised Hashing (SSH) has been proven effective since it integrates both labeled and unlabeled data to leverage semantic similarity while keeping robust to overfitting. However, it ignores the global label information and the local structure of the feature space. In this paper, we concentrate on these two issues and propose a novel semi-supervised hashing method called Locality Preserving Discriminative Hashing which combines two classical dimensionality reduction approaches, Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). The proposed method presents a rigorous formulation in which the supervised term tries to maintain the global information of the labeled data while the unsupervised term provides effective regularization to model local relationships of the unlabeled data. We apply an efficient sequential procedure to learn the hashing functions. Experimental comparisons with other state-of-the-art methods on three large scale datasets demonstrate the effectiveness and efficiency of our method.