# Communication-efficient Distributed Sparse Linear Discriminant Analysis

arXiv: https://arxiv.org/abs/1610.04798 · HTML: https://mlsanity.com/paper/1610.04798/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2016.

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

- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Distributed Sparse Multicategory Discriminant Analysis](https://mlsanity.com/paper/2202.10913/) (2022)

## Abstract (arXiv)

We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size N into m machines, and estimates a local sparse LDA estimator on each machine using the data subset of size N/m. After the distributed estimation, our method aggregates the debiased local estimators from m machines, and sparsifies the aggregated estimator. We show that the aggregated estimator attains the same statistical rate as the centralized estimation method, as long as the number of machines m is chosen appropriately. Moreover, we prove that our method can attain the model selection consistency under a milder condition than the centralized method. Experiments on both synthetic and real datasets corroborate our theory.