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Hashing as Tie-Aware Learning to Rank
[article]
2018
arXiv
pre-print
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to
arXiv:1705.08562v4
fatcat:uzo3q6h2cranjlmhw7qle7kwlm