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Locality preserving hashing
2014 IEEE International Conference on Image Processing (ICIP)
The spectral hashing algorithm relaxes and solves an objective function for generating hash codes such that data similarity is preserved in the Hamming space. However, the assumption of uniform global data distribution limits its applicability. In the paper, we introduce locality preserving projection to determine the data distribution adaptively, and a spectral method is adopted to estimate the eigenfunctions of the underlying graph Laplacian. Furthermore, pairwise label similarity can bedoi:10.1109/icip.2014.7025604 dblp:conf/icip/TsaiY14 fatcat:gm4aolnjzjfcpotvm3n5v7fisi