Multi-Party Sparse Discriminant Learning
Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature selection and classification. With the increasing needs of distributed data collection, storage and processing, enabling the Sparse Discriminant Learning to embrace the Multi-Party distributed computing environments becomes an emerging research topic. This paper proposes a novel Multi-Party SDA algorithm, which can learn SDA models effectively without sharing any raw data and basic statistics among machines. The proposed algorithm 1) leverages the direct estimation of SDA  to derive a distributed loss function for the discriminant learning, 2) parameterizes the distributed loss function with local/global estimates through bootstrapping, and 3) approximates a global estimation of linear discriminant projection vector by optimizing the "distributed bootstrapping loss function" with gossip-based stochastic gradient descent. Experimental results on both synthetic and real-world benchmark datasets show that our algorithm can compete with the centralized SDA with similar performance, and significantly outperforms the most recent distributed SDA  in terms of accuracy and F1-score.
J. Bian et al., "Multi-Party Sparse Discriminant Learning," Proceedings of the 17th IEEE International Conference on Data Mining (2017, New Orleans, LA), Institute of Electrical and Electronics Engineers (IEEE), Nov 2017.
The definitive version is available at https://doi.org/10.1109/ICDM.2017.86
17th IEEE International Conference on Data Mining, ICDM (2017: Nov. 18-21, New Orleans, LA)
Mathematics and Statistics
Keywords and Phrases
Multi-Party Statistical Learning; Sparse Discriminant Analysis; Bayesian Asymptotic Efficiency
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
21 Nov 2017