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The group fused Lasso for multiple change-point detection
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We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are proposed to solve the resulting optimization problems, either exactly or approximately. Conditions are given for consistency of both algorithms as the number of signals increases, and empirical evidence is provided to support the results on simulated and array comparative genomic hybridization data.
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Cited by 2 Pith papers
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Online change point detection under heavy-tailedness and contamination
Provides theoretical characterizations of detection delay for univariate online robust mean change point detection under Huber contamination and heavy tails, plus a multivariate robust mean testing procedure, with mat...
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focus and focus-cpt: Fast Online Changepoint Detection in R and Python
A single R/Python package now implements the focus family of exact online changepoint detectors, with convex-hull pruning and a claimed per-iteration cost of O(log(n)^d) for d-dimensional data.
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