Topic driven multimodal similarity learning with multi-view voted convolutional features
作者：
Highlights：
• A novel similarity learning model with layered architecture.
• The representation layer preserves a multi-view voted local neighbour structure.
• The multimodal layer computes distributional similarity over sparse relation types.
• The hidden relation neurons are initialized by cluster centres to encode topics.
• Comparison with seven competing methods shows effectiveness of the proposed model.
摘要
•A novel similarity learning model with layered architecture.•The representation layer preserves a multi-view voted local neighbour structure.•The multimodal layer computes distributional similarity over sparse relation types.•The hidden relation neurons are initialized by cluster centres to encode topics.•Comparison with seven competing methods shows effectiveness of the proposed model.
论文关键词：Convolutional auto-encoder,Representation learning,Multi-view learning,Multimodal similarity learning
论文评审过程：Received 12 September 2016, Revised 16 January 2017, Accepted 28 February 2017, Available online 9 March 2017, Version of Record 21 November 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.035
