# Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data

arXiv: https://arxiv.org/abs/1807.01422 · HTML: https://mlsanity.com/paper/1807.01422/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data](https://mlsanity.com/paper/2109.14171/) (2021)

## Abstract (arXiv)

We introduce a new method of performing high dimensional discriminant analysis, which we call multiDA. We achieve this by constructing a hybrid model that seamlessly integrates a multiclass diagonal discriminant analysis model and feature selection components. Our feature selection component naturally simplifies to weights which are simple functions of likelihood ratio statistics allowing natural comparisons with traditional hypothesis testing methods. We provide heuristic arguments suggesting desirable asymptotic properties of our algorithm with regards to feature selection. We compare our method with several other approaches, showing marked improvements in regard to prediction accuracy, interpretability of chosen features, and algorithm run time. We demonstrate such strengths of our model by showing strong classification performance on publicly available high dimensional datasets, as well as through multiple simulation studies. We make an R package available implementing our approach.