Functional Labeled Optimal Partitioning

TL;DR

This paper introduces FLOPART, a dynamic programming algorithm for peak detection that ensures zero train label errors and high test accuracy, improving over existing methods in both training and testing phases.

Contribution

FLOPART is a novel dynamic programming algorithm that guarantees zero train label errors while providing accurate test set predictions for peak detection.

Findings

FLOPART achieves zero train label errors.

FLOPART is more accurate than existing algorithms.

FLOPART has similar time complexity to current methods.

Abstract

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.