I'm looking at Fisher's LDA on various datasets on UCI ML repository and trying to see where LDA might perform badly. One reason I can think of is if the data distribution is not a multi-variate normal distribution. This is from the fact I read in a book where you apply LDA on multivariate normal distribution. Is that thought process correct? When might LDA give bad results?

**Contents**hide

#### Best Answer

By definition the random vector $X$ is multivariate normal if **all** linear combinations $a^T X$ have some (univariate) normal distribution. So one idea to test multivariate normality is to search among the vectors $a$ for one such that $a^T X$ is definitely not normal. That is the idea behind pp, projection pursuit methods. See https://en.wikipedia.org/wiki/Projection_pursuit

### Similar Posts:

- Solved – How to detect if a given dataset has multivariate normal distribution
- Solved – If any two variables follow a normal bivariate distribution does it also have a multivariate normal distribution
- Solved – Book about the normal distribution and multivariate normal distribution
- Solved – Multivariate logistic distribution
- Solved – Multivariate logistic distribution