# Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization

arXiv: https://arxiv.org/abs/2104.08112 · HTML: https://mlsanity.com/paper/2104.08112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Training Deep Networks with Structured Layers by Matrix Backpropagation](https://mlsanity.com/paper/1509.07838/) (2015)
- [Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning](https://mlsanity.com/paper/1808.02229/) (2018)
- [Cascaded Projection: End-to-End Network Compression and Acceleration](https://mlsanity.com/paper/1903.04988/) (2019)

## Abstract (arXiv)

Linear Discriminant Analysis (LDA) is commonly used for dimensionality reduction in pattern recognition and statistics. It is a supervised method that aims to find the most discriminant space of reduced dimension that can be further used for classification. In this work, we present a Grassmann Iterative LDA method (GILDA) that is based on Proxy Matrix Optimization (PMO). PMO makes use of automatic differentiation and stochastic gradient descent (SGD) on the Grassmann manifold to arrive at the optimal projection matrix. Our results show that GILDAoutperforms the prevailing manifold optimization method.