# A New Reduced-Rank Linear Discriminant Analysis Method and Its Applications

arXiv: https://arxiv.org/abs/1511.00282 · HTML: https://mlsanity.com/paper/1511.00282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2015.

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

- [Classification of high-dimensional data with spiked covariance matrix structure](https://mlsanity.com/paper/2110.01950/) (2021)

## Abstract (arXiv)

We consider multi-class classification problems for high dimensional data. Following the idea of reduced-rank linear discriminant analysis (LDA), we introduce a new dimension reduction tool with a flavor of supervised principal component analysis (PCA). The proposed method is computationally efficient and can incorporate the correlation structure among the features. Besides the theoretical insights, we show that our method is a competitive classification tool by simulated and real data examples.