# Large-Sample Properties of Blind Estimation of the Linear Discriminant Using Projection Pursuit

arXiv: https://arxiv.org/abs/2103.04678 · HTML: https://mlsanity.com/paper/2103.04678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

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

- [Search Dimension in Unlabeled Projection Pursuit: A Scaling Law for Subspace Restriction](https://mlsanity.com/paper/2609.37917/) (2026)

## Abstract (arXiv)

We study the estimation of the linear discriminant with projection pursuit, a method that is blind in the sense that it does not use the class labels in the estimation. Our viewpoint is asymptotic and, as our main contribution, we derive central limit theorems for estimators based on three different projection indices, skewness, kurtosis and their convex combination. The results show that in each case the limiting covariance matrix is proportional to that of linear discriminant analysis (LDA), an unblind estimator of the discriminant. An extensive comparative study between the asymptotic variances reveals that projection pursuit is able to achieve efficiency equal to LDA when the groups are arbitrarily well-separated and their sizes are reasonably balanced. We conclude with a real data example and a simulation study investigating the validity of the obtained asymptotic formulas for finite samples.