# Multiclass Sparse Discriminant Analysis

arXiv: https://arxiv.org/abs/1504.05845 · HTML: https://mlsanity.com/paper/1504.05845/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2015.

## Built on by (most cited first, 8 of 8)

- [Optimal Discriminant Analysis in High-Dimensional Latent Factor Models](https://mlsanity.com/paper/2210.12862/) (2022)
- [Multiclass classification for multidimensional functional data through deep neural networks](https://mlsanity.com/paper/2305.13349/) (2023)
- [Classification of high-dimensional data with spiked covariance matrix structure](https://mlsanity.com/paper/2110.01950/) (2021)
- [Linear Discriminant Analysis with High-dimensional Mixed Variables](https://mlsanity.com/paper/2112.07145/) (2021)
- [Distributed Sparse Multicategory Discriminant Analysis](https://mlsanity.com/paper/2202.10913/) (2022)
- [Varying Coefficient Linear Discriminant Analysis for Dynamic Data](https://mlsanity.com/paper/2203.06371/) (2022)
- [Hierarchical Quadratic Random Forest Classifier](https://mlsanity.com/paper/2306.01893/) (2023)
- [Multi-Group Quadratic Discriminant Analysis via Projection](https://mlsanity.com/paper/2601.05415/) (2026)

## Abstract (arXiv)

In recent years many sparse linear discriminant analysis methods have been proposed for high-dimensional classification and variable selection. However, most of these proposals focus on binary classification and they are not directly applicable to multiclass classification problems. There are two sparse discriminant analysis methods that can handle multiclass classification problems, but their theoretical justifications remain unknown. In this paper, we propose a new multiclass sparse discriminant analysis method that estimates all discriminant directions simultaneously. We show that when applied to the binary case our proposal yields a classification direction that is equivalent to those by two successful binary sparse LDA methods in the literature. An efficient algorithm is developed for computing our method with high-dimensional data. Variable selection consistency and rates of convergence are established under the ultrahigh dimensionality setting. We further demonstrate the superior performance of our proposal over the existing methods on simulated and real data.