Collaborative multiview hashing
作者：
Highlights：
• We exploit the diverse information of multiview representations by utilizing the collaboration between view representations to learn the binary codes in each view such that they are predictive to each other.
• We exploit the correlation between the view representations and the semantic labels to preserve the semantic relationship between data samples.
• We employ nonlinear hashing functions as the projection in each view to preserve the local data structure in each view.
摘要
•We exploit the diverse information of multiview representations by utilizing the collaboration between view representations to learn the binary codes in each view such that they are predictive to each other.•We exploit the correlation between the view representations and the semantic labels to preserve the semantic relationship between data samples.•We employ nonlinear hashing functions as the projection in each view to preserve the local data structure in each view.
论文关键词：Multiview hashing,View collaboration,Nonlinear hashing,Binary code
论文评审过程：Received 1 November 2016, Revised 17 February 2017, Accepted 22 February 2017, Available online 28 February 2017, Version of Record 21 November 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.026
