# Predicting Future Cognitive Decline with Hyperbolic Stochastic Coding

arXiv: https://arxiv.org/abs/2102.10503 · HTML: https://mlsanity.com/paper/2102.10503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 107 references that are in the map.
- First posted on arXiv 21 Feb 2021.

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

- [Fast Low-rank Shared Dictionary Learning for Image Classification](https://mlsanity.com/paper/1610.08606/) (2016)
- [Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging](https://mlsanity.com/paper/1707.05961/) (2017)
- [Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation](https://mlsanity.com/paper/1407.8147/) (2014)
- [Regularized Wasserstein Means for Aligning Distributional Data](https://mlsanity.com/paper/1812.00338/) (2018)

## Abstract (arXiv)

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However such approaches, similar to other surface based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.