Multi-modal classification of Alzheimer's disease using nonlinear graph fusion
作者：
Highlights：
• Multi-modality biomarkers were used for the classification of AD.
• Nonlinear graph fusion was used to investigate the multi-modal complementary information.
• Validations were performed in different classification scenarios.
• We achieved superior results than the state-of-the-art linear combination approaches.
• The proposed method provides an effective way to integrate multiple heterogeneous data for the classification of AD.
摘要
Highlights•Multi-modality biomarkers were used for the classification of AD.•Nonlinear graph fusion was used to investigate the multi-modal complementary information.•Validations were performed in different classification scenarios.•We achieved superior results than the state-of-the-art linear combination approaches.•The proposed method provides an effective way to integrate multiple heterogeneous data for the classification of AD.
论文关键词：Multiple modalities,Biomarkers,Nonlinear graph fusion,Machine learning,Classification of Alzheimer's disease
论文评审过程：Received 24 May 2016, Revised 13 September 2016, Accepted 6 October 2016, Available online 8 October 2016, Version of Record 15 October 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.10.009
