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• W2604269544 abstract "Hashing has been proven a promising technique for fast nearest neighbor search over massive databases. In many practical tasks it usually builds multiple hash tables for a desired level of recall performance. However, existing multi-table hashing methods suffer from the heavy table redundancy, without strong table complementarity and effective hash code learning. To address the problem, this paper proposes a multi-table learning method which pursues a specified number of complementary and informative hash tables from a perspective of ensemble learning. By regarding each hash table as a neighbor prediction model, the multi-table search procedure boils down to a linear assembly of predictions stemming from multiple tables. Therefore, a sequential updating and learning framework is naturally established in a boosting mechanism, theoretically guaranteeing the table complementarity and algorithmic convergence. Furthermore, each boosting round pursues the discriminative hash functions for each table by a discrete optimization in the binary code space. Extensive experiments carried out on two popular tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed boosted complementary hash-tables method enjoys the strong table complementarity and significantly outperforms the state-of-the-arts." @default.
• W2604269544 created "2017-04-14" @default.
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• W2604269544 date "2017-02-12" @default.
• W2604269544 modified "2023-10-16" @default.
• W2604269544 title "Boosting Complementary Hash Tables for Fast Nearest Neighbor Search" @default.
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• W2604269544 doi "https://doi.org/10.1609/aaai.v31i1.11204" @default.
• W2604269544 hasPublicationYear "2017" @default.
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