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• 367 creator dhruv-mahajan.
• 367 creator sundararajan-sellamanickam.
• 367 creator vinod-nair.
• 367 type InProceedings.
• 367 label "A Unified Approach to Learning Task-Specific Bit Vector Representations for Fast, Nearest Neighbour Search".
• 367 sameAs 367.
• 367 abstract "Fast nearest neighbor search is necessary for large scale web-applications, such as information retrieval, classification and 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 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 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. RankNet and LambdaRank algorithms are used for learning a function that computes a task-specific bit vector from an input data vector. Our algorithm 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 a day.".
• 367 hasAuthorList authorList.
• 367 isPartOf proceedings.
• 367 keyword "Classification".
• 367 keyword "Fast Nearest Neighbor Search".
• 367 keyword "Hashing".
• 367 keyword "Lambdarank".
• 367 keyword "Learning".
• 367 keyword "Ranknet".
• 367 keyword "Retrieval".
• 367 title "A Unified Approach to Learning Task-Specific Bit Vector Representations for Fast, Nearest Neighbour Search".