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 correlation analysis (CCA) is an important method for multiple feature extraction and fusion. The canonical projective vectors in classical CCA method satisfy conjugated orthogonality constraints. However, the conjugated orthogonality property is badly affected by the small sample size (SSS) problem so that the projections in the classical CCA(More)
Multiset canonical correlation analysis (MCCA) aims at revealing the linear correlations among multiple sets of high-dimensional data. Therefore, it is only a linear multiview dimensionality reduction technique and such a linear model is insufficient to discover the nonlinear correlation information hidden in multiview data. In this paper, we incorporate(More)
Multiset canonical correlation analysis (MCCA) can simultaneously reduce the dimensionality of multi-set data. Thus, MCCA is a very important method for multiple feature extraction. However, in small sample size problem, covariance matrix cannot be estimated accurately so that the projections in MCCA are usually not optimal in such case for recognition(More)
Hashing techniques have been widely applied to large-scale cross-view retrieval tasks due to the significant advantage of binary codes in computation and storage efficiency. However, most existing cross-view hashing methods learn binary codes with continuous relaxations, which cause large quantization loss across views. To address this problem, in this(More)