Nested cross-validation based adaptive sparse representation algorithm and its application to pathological brain classification
作者：
Highlights：
• A novel NCVASR technique for pathological brain classification is proposed.
• It uses sparse representation with nested cross validation technique.
• It solves the overfitting and bias problems in the classification task.
• A new adaptively tuned classifier is proposed.
• Experimental results on real brain database is presented.
摘要
•A novel NCVASR technique for pathological brain classification is proposed.•It uses sparse representation with nested cross validation technique.•It solves the overfitting and bias problems in the classification task.•A new adaptively tuned classifier is proposed.•Experimental results on real brain database is presented.
论文关键词：Pathological brain classification,Gray level co-occurrence matrix,Nested cross-validation based adaptive sparse representation algorithm,Nested cross-validation technique
论文评审过程：Received 21 February 2018, Revised 16 July 2018, Accepted 17 July 2018, Available online 25 July 2018, Version of Record 4 August 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.039
