# Functional Labeled Optimal Partitioning

arXiv: https://arxiv.org/abs/2210.02580 · HTML: https://mlsanity.com/paper/2210.02580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [On Optimal Multiple Changepoint Algorithms for Large Data](https://mlsanity.com/paper/1409.1842/) (2014)
- [Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models](https://mlsanity.com/paper/2012.06848/) (2020)
- [Labeled Optimal Partitioning](https://mlsanity.com/paper/2006.13967/) (2020)

## Abstract (arXiv)

Peak detection is a problem in sequential data analysis that involves differentiating regions with higher counts (peaks) from regions with lower counts (background noise). It is crucial to correctly predict areas that deviate from the background noise, in both the train and test sets of labels. Dynamic programming changepoint algorithms have been proposed to solve the peak detection problem by constraining the mean to alternatively increase and then decrease. The current constrained changepoint algorithms only create predictions on the test set, while completely ignoring the train set. Changepoint algorithms that are both accurate when fitting the train set, and make predictions on the test set, have been proposed but not in the context of peak detection models. We propose to resolve these issues by creating a new dynamic programming algorithm, FLOPART, that has zero train label errors, and is able to provide highly accurate predictions on the test set. We provide an empirical analysis that shows FLOPART has a similar time complexity while being more accurate than the existing algorithms in terms of train and test label errors.