Functional Labeled Optimal Partitioning
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. Dyna...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2210.02580 |