Robust hashing for multi-view data: Jointly learning low-rank kernelized similarity consensus and hash functions

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• 作者：Lin Wu^a ; ^b ; ^{lin.wu@adelaide.edu.au} ; Yang Wang^c ; ^{wangy@cse.unsw.edu.au}
• 关键词：Multiple feature learning ; Robust hashing ; Low-rank recovery
• 刊名：Image and Vision Computing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：57
• 期：Complete
• 页码：58-66
• 全文大小：1128 K
• 卷排序：57

文摘

A robust hashing method for multi-view data with noise corruptions is presented. It is to jointly learn a low-rank kernelized similarity consensus and hash functions. Approximate landmark graph is employed to make training fast. Extensive experiments are conducted on benchmarks to show the efficacy of our model.