Xiao-Bo Shen

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Hashing techniques have attracted broad research interests in recent multimedia studies. However, most of existing hashing methods focus on learning binary codes from data with only one single view, and thus cannot fully utilize the rich information from multiple views of data. In this paper, we propose a novel unsupervised hashing approach, dubbed(More)
canonical correlations (M2CCs) framework for subspace learning. In the proposed framework, the input data of each original view are mapped into multiple higher dimensional feature spaces by multiple nonlinear mappings determined by different kernels. This makes M2CC can discover multiple kinds of useful information of each original view in the feature(More)