Distributed Sparse Multicategory Discriminant Analysis

Hengchao Chen, Qiang Sun
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:604-624, 2022.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-chen22b, title = { Distributed Sparse Multicategory Discriminant Analysis }, author = {Chen, Hengchao and Sun, Qiang}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {604--624}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/chen22b/chen22b.pdf}, url = {https://proceedings.mlr.press/v151/chen22b.html}, abstract = { 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. } }
Endnote
%0 Conference Paper %T Distributed Sparse Multicategory Discriminant Analysis %A Hengchao Chen %A Qiang Sun %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-chen22b %I PMLR %P 604--624 %U https://proceedings.mlr.press/v151/chen22b.html %V 151 %X 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.
APA
Chen, H. & Sun, Q.. (2022). Distributed Sparse Multicategory Discriminant Analysis . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:604-624 Available from https://proceedings.mlr.press/v151/chen22b.html.

Related Material