2022-06-28-bartan22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time	Proceedings of the 39th International Conference on Machine Learning	Fisher’s Linear Discriminant Analysis (FLDA) is a statistical analysis method that linearly embeds data points to a lower dimensional space to maximize a discrimination criterion such that the variance between classes is maximized while the variance within classes is minimized. We introduce a natural extension of FLDA that employs neural networks, called Neural Fisher Discriminant Analysis (NFDA). This method finds the optimal two-layer neural network that embeds data points to optimize the same discrimination criterion. We use tools from convex optimization to transform the optimal neural network embedding problem into a convex problem. The resulting problem is easy to interpret and solve to global optimality. We evaluate the method’s performance on synthetic and real datasets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bartan22a	0	Neural {F}isher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time	1647	1663	1647-1663	1647	false	Bartan, Burak and Pilanci, Mert	given family Burak Bartan given family Mert Pilanci	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/bartan22a/bartan22a.pdf	label link Other Files https://media.icml.cc/Conferences/ICML2022/supplementary/bartan22a-supp.zip