Cross-view hashing via supervised deep discrete matrix factorization

作者：

Highlights：

• We propose a discrete deep matrix factorization to learn unified hash codes which can capture more complex structure within heterogeneous data, resulting in more representative hashing codes.

• We further introduce a linear classification error term to make the learned unified hashing codes discriminative.

• We directly conduct discrete optimization which can reduce the quantization error.

摘要

•We propose a discrete deep matrix factorization to learn unified hash codes which can capture more complex structure within heterogeneous data, resulting in more representative hashing codes.•We further introduce a linear classification error term to make the learned unified hashing codes discriminative.•We directly conduct discrete optimization which can reduce the quantization error.

论文关键词：Matrix factorization,Cross-view hashing,Similarity search

论文评审过程：Received 1 August 2019, Revised 27 January 2020, Accepted 10 February 2020, Available online 11 February 2020, Version of Record 18 February 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107270