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- W2964058067 abstract "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." @default.
- W2964058067 created "2019-07-30" @default.
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- W2964058067 date "2017-04-10" @default.
- W2964058067 modified "2023-09-27" @default.
- W2964058067 title "Communication-efficient Distributed Sparse Linear Discriminant Analysis" @default.
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