# Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification

arXiv: https://arxiv.org/abs/2108.05761 · HTML: https://mlsanity.com/paper/2108.05761/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 80 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning](https://mlsanity.com/paper/1811.02316/) (2018)
- [View selection in multi-view stacking: Choosing the meta-learner](https://mlsanity.com/paper/2010.16271/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data](https://mlsanity.com/paper/2401.04579/) (2024)
- [Imputation of missing values in multi-view data](https://mlsanity.com/paper/2210.14484/) (2022)

## Abstract (arXiv)

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.