Complex heterogeneity learning: A theoretical and empirical study
作者：
Highlights：
• A graph based hybrid approach to model task relatedness, view consistency, and bag instance correlations in a principled framework.
• An iterative learning algorithm to solve the non convex non smooth optimization problem.
• Theoretical analysis in terms of Rademacher complexity showing the improvement of generalization performance by jointly modeling triple heterogeneity.
• Experimental results on various data sets demonstrating the effectiveness of the proposed algorithm.
摘要
•A graph based hybrid approach to model task relatedness, view consistency, and bag instance correlations in a principled framework.•An iterative learning algorithm to solve the non convex non smooth optimization problem.•Theoretical analysis in terms of Rademacher complexity showing the improvement of generalization performance by jointly modeling triple heterogeneity.•Experimental results on various data sets demonstrating the effectiveness of the proposed algorithm.
论文关键词：Heterogeneous learning,Multi-task learning,Multi-view learning,Multi-instance learning
论文评审过程：Received 30 October 2019, Revised 12 May 2020, Accepted 23 June 2020, Available online 2 July 2020, Version of Record 6 July 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107519
