Group-constrained manifold learning: Application to AD risk assessment

作者：

Highlights：

• Laplacian Eigenmaps and Isomap novel formulation to incorporate group-constraints.

• Improved time-to-conversion and MMSE estimation of MCI to AD converter subjects.

• Coherent movement of the subjects in low-dimensional space as disease progresses.

摘要

Highlights•Laplacian Eigenmaps and Isomap novel formulation to incorporate group-constraints.•Improved time-to-conversion and MMSE estimation of MCI to AD converter subjects.•Coherent movement of the subjects in low-dimensional space as disease progresses.

论文关键词：Alzheimer's disease,Manifold learning,Longitudinal,Neurodegeneration

论文评审过程：Received 2 December 2015, Revised 13 June 2016, Accepted 21 September 2016, Available online 28 September 2016, Version of Record 27 November 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.023