From aging to early-stage Alzheimer's: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning
作者：
Highlights：
• We propose a paradigm unveiling handwriting changes due to aging and Alzheimer‘s.
• Our new semi-supervised learning and sequential representation learning are key.
• Semi-supervised learning brings to light handwriting multimodal behavioral trends.
• Our sequential representation learning uncovers temporal feature representations.
• Classification based on temporal representations outperforms the state of the art.
摘要
•We propose a paradigm unveiling handwriting changes due to aging and Alzheimer‘s.•Our new semi-supervised learning and sequential representation learning are key.•Semi-supervised learning brings to light handwriting multimodal behavioral trends.•Our sequential representation learning uncovers temporal feature representations.•Classification based on temporal representations outperforms the state of the art.
论文关键词：Online handwriting,Alzheimer’s,Mild Cognitive Impairment,Aging,Unsupervised & semi-supervised learning,Temporal representation learning
论文评审过程：Received 30 November 2017, Revised 3 July 2018, Accepted 31 July 2018, Available online 16 August 2018, Version of Record 20 September 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.07.029
