Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing - Cong PENG; Jiangbo QIAN; Huahui CHEN; Yihong DONG (Dianxin kexue)
Because of efficiency in query and storage,learning hash is applied in solving the nearest neighbor search problem.The learning hash usually converts high-dimensional data into binary codes.In this way,the similarities between binary codes from two objects are conserved as they were in the original high-dimensional space.In practical applications,a lot of data which have the same distance from the query point but with different code will be returned.How to reorder these candidates is a problem.An algorithm named weighted self-taught hashing was proposed.Experimental results show that the proposed algorithm can reorder the different binary codes with the same Hamming distances efficiently.Compared to the naive algorithm,the F1-score of the proposed algorithm is improved by about 2 times and it is better than the homologous algorithms,furthermore,the time cost is reduced by an order of magnitude.