# Distributed Sparse Multicategory Discriminant Analysis

arXiv: https://arxiv.org/abs/2202.10913 · HTML: https://mlsanity.com/paper/2202.10913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Efficient Distributed Learning with Sparsity](https://mlsanity.com/paper/1605.07991/) (2016)
- [Randomized Sketches of Convex Programs with Sharp Guarantees](https://mlsanity.com/paper/1404.7203/) (2014)
- [Distributed Inference for Linear Support Vector Machine](https://mlsanity.com/paper/1811.11922/) (2018)
- [Multiclass Sparse Discriminant Analysis](https://mlsanity.com/paper/1504.05845/) (2015)
- [Simultaneous sparse estimation of canonical vectors in the p>>N setting](https://mlsanity.com/paper/1403.6095/) (2014)
- [Communication-efficient Distributed Sparse Linear Discriminant Analysis](https://mlsanity.com/paper/1610.04798/) (2016)

## Abstract (arXiv)

This paper proposes a convex formulation for sparse multicategory linear discriminant analysis and then extend it to the distributed setting when data are stored across multiple sites. The key observation is that for the purpose of classification it suffices to recover the discriminant subspace which is invariant to orthogonal transformations. Theoretically, we establish statistical properties ensuring that the distributed sparse multicategory linear discriminant analysis performs as good as the centralized version after {a few rounds} of communications. Numerical studies lend strong support to our methodology and theory.