# Varying Coefficient Linear Discriminant Analysis for Dynamic Data

arXiv: https://arxiv.org/abs/2203.06371 · HTML: https://mlsanity.com/paper/2203.06371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [High-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data](https://mlsanity.com/paper/1804.03018/) (2018)
- [Multiclass Sparse Discriminant Analysis](https://mlsanity.com/paper/1504.05845/) (2015)
- [Dynamic Linear Discriminant Analysis in High Dimensional Space](https://mlsanity.com/paper/1708.00205/) (2017)
- [Semiparametric Sparse Discriminant Analysis](https://mlsanity.com/paper/1304.4983/) (2013)

## Abstract (arXiv)

Linear discriminant analysis (LDA) is an important classification tool in statistics and machine learning. This paper investigates the varying coefficient LDA model for dynamic data, with Bayes' discriminant direction being a function of some exposure variable to address the heterogeneity. We propose a new least-square estimation method based on the B-spline approximation. The data-driven discriminant procedure is more computationally efficient than the dynamic linear programming rule citepjiang2020dynamic. We also establish the convergence rates for the corresponding estimation error bound and the excess misclassification risk. The estimation error in L2 distance is optimal for the low-dimensional regime and is near optimal for the high-dimensional regime. Numerical experiments on synthetic data and real data both corroborate the superiority of our proposed classification method.