document: A unified approach to learning task-specific bit vector representations for fast nearest neighbor search Fast nearest neighbor search is necessary for a variety of large scale web applications such as information retrieval , nearest neighbor classification and nearest neighbor regression . Recently a number of machine learning algorithms have been proposed for representing the data to be searched as ( short ) bit vectors and then using hashing to do rapid search . These algorithms have been limited in their applicability in that they are suited for only one type of task -- e.g. Spectral Hashing learns bit vector representations for retrieval , but not say , classification . In this paper we present a unified approach to learning bit vector representations for many applications that use nearest neighbor search . The main contribution is a single learning algorithm that can be customized to learn a bit vector representation suited for the task at hand . This broadens the usefulness of bit vector representations to tasks beyond just conventional retrieval . We propose a learning-to-rank formulation to learn the bit vector representation of the data . LambdaRank algorithm is used for learning a function that computes a task-specific bit vector from an input data vector . Our approach outperforms state-of-the-art nearest neighbor methods on a number of real world text and image classification and retrieval datasets . It is scalable and learns a 32-bit representation on 1.46 million training cases in two days .
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