# Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities

arXiv: https://arxiv.org/abs/1604.06310 · HTML: https://mlsanity.com/paper/1604.06310/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2016.

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

- [Review of Clustering Methods for Functional Data](https://mlsanity.com/paper/2210.00847/) (2022)

## Abstract (arXiv)

We propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operators. Then, subsequent applications including a k sample test for equality of covariance, a functional data classifier, and an expectation-maximization style clustering algorithm are derived and tested on both simulated and phoneme data.