Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification

1Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

Cite

CITATION STYLE

APA

van Loon, W., de Vos, F., Fokkema, M., Szabo, B., Koini, M., Schmidt, R., & de Rooij, M. (2022). Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer’s Disease Classification. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.830630

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free