Multimodal subspace support vector data description
作者：
Highlights：
• A novel method for transforming the multimodal data into a common feature space is proposed.
• The shared subspace optimized for one-class classification yields better results than traditional concatenation of multimodal data for one-class classification.
• Different regularization strategies along with linear and non-linear formulation provides more freedom of choice for optimizing a model according to specific evaluation metric.
摘要
•A novel method for transforming the multimodal data into a common feature space is proposed.•The shared subspace optimized for one-class classification yields better results than traditional concatenation of multimodal data for one-class classification.•Different regularization strategies along with linear and non-linear formulation provides more freedom of choice for optimizing a model according to specific evaluation metric.
论文关键词：Feature transformation,Multimodal data,One-class classification,Support vector data description,Subspace learning
论文评审过程：Received 21 August 2019, Revised 13 July 2020, Accepted 6 September 2020, Available online 10 September 2020, Version of Record 17 September 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107648
