# Sensible Functional Linear Discriminant Analysis

arXiv: https://arxiv.org/abs/1606.03844 · HTML: https://mlsanity.com/paper/1606.03844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2016.

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

- [Functional sufficient dimension reduction through information maximization with application to classification](https://mlsanity.com/paper/2305.10880/) (2023)

## Abstract (arXiv)

The focus of this paper is to extend Fisher's linear discriminant analysis (LDA) to both densely re-corded functional data and sparsely observed longitudinal data for general c-category classification problems. We propose an efficient approach to identify the optimal LDA projections in addition to managing the noninvertibility issue of the covariance operator emerging from this extension. A conditional expectation technique is employed to tackle the challenge of projecting sparse data to the LDA directions. We study the asymptotic properties of the proposed estimators and show that asymptotically perfect classification can be achieved in certain circumstances. The performance of this new approach is further demonstrated with numerical examples.