Reconstruction regularized low-rank subspace learning for cross-modal retrieval
作者：
Highlights：
• The novel reconstruction regularization term can preserve the essential information.
• Low-rank constraint can well explore the correlation among samples.
• An efficient algorithm is presented to optimize the problem with convergence guarantee.
• RRLSL can be applied to both supervised and unsupervised situations.
• Superior performance is achieved with lowest computational complexity.
摘要
•The novel reconstruction regularization term can preserve the essential information.•Low-rank constraint can well explore the correlation among samples.•An efficient algorithm is presented to optimize the problem with convergence guarantee.•RRLSL can be applied to both supervised and unsupervised situations.•Superior performance is achieved with lowest computational complexity.
论文关键词：Cross-modal retrieval,Low-rank subspace learning,Reconstruction regularization
论文评审过程：Received 11 March 2020, Revised 6 November 2020, Accepted 7 December 2020, Available online 12 January 2021, Version of Record 20 January 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107813
