Principled change point detection via representation learning

06/04/2021
by   Evgenia Romanenkova, et al.
0

Change points are abrupt alterations in the distribution of sequential data. A change-point detection (CPD) model aims at quick detection of such changes. Classic approaches perform poorly for semi-structured sequential data because of the absence of adequate data representation learning. To deal with it, we introduce a principled differentiable loss function that considers the specificity of the CPD task. The theoretical results suggest that this function approximates well classic rigorous solutions. For such loss function, we propose an end-to-end method for the training of deep representation learning CPD models. Our experiments provide evidence that the proposed approach improves baseline results of change point detection for various data types, including real-world videos and image sequences, and improve representations for them.

READ FULL TEXT

Authors

page 15

04/15/2022

Deep learning model solves change point detection for multiple change types

A change points detection aims to catch an abrupt disorder in data distr...
04/18/2022

Usage of specific attention improves change point detection

The change point is a moment of an abrupt alteration in the data distrib...
03/29/2022

Graph similarity learning for change-point detection in dynamic networks

Dynamic networks are ubiquitous for modelling sequential graph-structure...
08/21/2020

Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation

Change point detection (CPD) aims to locate abrupt property changes in t...
10/21/2020

Network topology change-point detection from graph signals with prior spectral signatures

We consider the problem of sequential graph topology change-point detect...
10/22/2019

Continual Learning for Infinite Hierarchical Change-Point Detection

Change-point detection (CPD) aims to locate abrupt transitions in the ge...
05/18/2018

Change Point Methods on a Sequence of Graphs

The present paper considers a finite sequence of graphs, e.g., coming fr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.