# Spatial Depth-Based Classification for Functional Data

arXiv: https://arxiv.org/abs/1305.2957 · HTML: https://mlsanity.com/paper/1305.2957/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2013.

## Built on by (most cited first, 2 of 2)

- [Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning](https://mlsanity.com/paper/2309.11067/) (2023)
- [seMCD: Sequentially implemented Monte Carlo depth computation with statistical guarantees](https://mlsanity.com/paper/2507.06227/) (2025)

## Abstract (arXiv)

We enlarge the number of available functional depths by introducing the kernelized functional spatial depth (KFSD). KFSD is a local-oriented and kernel-based version of the recently proposed functional spatial depth (FSD) that may be useful for studying functional samples that require an analysis at a local level. In addition, we consider supervised functional classification problems, focusing on cases in which the differences between groups are not extremely clear-cut or the data may contain outlying curves. We perform classification by means of some available robust methods that involve the use of a given functional depth, including FSD and KFSD, among others. We use the functional textitk-nearest neighbor classifier as a benchmark procedure. The results of a simulation study indicate that the KFSD-based classification approach leads to good results. Finally, we consider two real classification problems, obtaining results that are consistent with the findings observed with simulated curves.