# Linear discriminant initialization for feed-forward neural networks

arXiv: https://arxiv.org/abs/2007.12782 · HTML: https://mlsanity.com/paper/2007.12782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Mirror descent of Hopfield model](https://mlsanity.com/paper/2211.15880/) (2022)

## Abstract (arXiv)

Informed by the basic geometry underlying feed forward neural networks, we initialize the weights of the first layer of a neural network using the linear discriminants which best distinguish individual classes. Networks initialized in this way take fewer training steps to reach the same level of training, and asymptotically have higher accuracy on training data.