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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Multiple feature learning,Robust hashing,Low-rank recovery

论文评审过程：Received 3 May 2016, Revised 23 July 2016, Accepted 16 November 2016, Available online 24 November 2016, Version of Record 4 December 2016.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.11.008