On Matched Filtering for Statistical Change Point Detection

Cheng, Kevin; Miller, Eric L.; Hughes, Michael C.; Aeron, Shuchin

doi:10.1109/ojsp.2020.3035070

Cited by 8 publications

(10 citation statements)

References 42 publications

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“…The signal and noise in the point cloud data are randomly distributed, and their characteristics can often only be described in a statistical sense. The distance between each point in the point cloud model and the neighboring point obeys a certain statistical distribution law [28]. For sample point p, the large scale noise in the flat area, where the surface change factor is less than the average surface change factor, is removed by statistical filtering.…”

Section: ) Statistical Filteringmentioning

confidence: 99%

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Ren

et al. 2021

IEEE Access

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The multiscale noise in the 3D point cloud data of rock surfaces which collected by 3D scanners has a significant influence on the exploration of rock surface morphology. To this end, this paper proposes a multiscale noise removal overall filtering algorithm. The specific processing procedure of the algorithm is as follows. First, a weighted principal component analysis is performed on point cloud data, i.e., the neighboring point distance is used as a weight in the principal component analysis, the covariance feature matrix of the weighted point is estimated, and the eigenvector corresponding to the lowest eigenvalue is used as the normal vector of the point cloud data. Second, in the weighted principal component analysis, estimating three eigenvalues corresponding to the Eigen matrix of the point cloud data, the ratio of the eigenvalue corresponding to the normal vector to the sum of three eigenvalues is used as the surface change factor. For the sample point, if the surface change factor of one sample point is less than the average value of the surface change factor of all sample points in the neighborhood, the sample point belongs to a flat area; otherwise, it belongs to a mutation area. Finally, in order to achieve multiscale noise removal, statistical filtering algorithm is used to remove large scale noise in flat area, additionally bilateral filtering algorithm is adopted to remove small scale noise in mutation area. In the experiments, the improved principal component analysis is combined with the overall filtering algorithm to accurately estimate the eigenvalues of the point cloud data points. After that, the eigenvalues of the sample points are used to distinguish between flat area and mutation area, so as to consider large scale noise and small scale noise. From the experimental results, it can be seen that overall filtering algorithm can consider both large scale and small scale noise and can remove noise from the point cloud data of rock samples. Visual judgment, normal distribution and fractal distribution tests are employed on filtered rock sample point cloud data to verify the reliability of the filtering results.INDEX TERMS 3D scanner, point cloud data, multiscale noise, statistical filtering, bilateral filtering.

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Section: ) Statistical Filteringmentioning

confidence: 99%

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Ren

et al. 2021

IEEE Access

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“…Our Θ estimation problem is non-convex, so the solution is dependent on initialization. To ensure fair comparison, we use the same initialization for each method: a time-series clustering method (Cheng et al, 2020a) that applies matched-filtered change point detection (Cheng et al, 2020b) for the Wasserstein distance. Additional details regarding optimization and initialization are provided in the supplement.…”

Section: Datasets and Evaluation Proceduresmentioning

confidence: 99%

Dynamical Wasserstein Barycenters for Time-series Modeling

Cheng¹,

Aeron²,

Hughes³

et al. 2021

Preprint

Self Cite

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Many time series can be modeled as a sequence of segments representing highlevel discrete states, such as running and walking in a human activity application. Flexible models should describe the system state and observations in stationary "pure-state" periods as well as transition periods between adjacent segments, such as a gradual slowdown between running and walking. However, most prior work assumes instantaneous transitions between pure discrete states. We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner. Our model assumes each pure state generates data from a multivariate normal distribution, and characterizes transitions between states via displacement-interpolation specified by the Wasserstein barycenter. The system state is represented by a barycentric weight vector which evolves over time via a random walk on the simplex. Parameter learning leverages the natural Riemannian geometry of Gaussian distributions under the Wasserstein distance, which leads to improved convergence speeds. Experiments on several human activity datasets show that our proposed DWB model accurately learns the generating distribution of pure states while improving state estimation for transition periods compared to the commonly used linear interpolation mixture models.1 Code available at https://github.com/kevin-c-cheng/DynamicalWassBarycenters_Gaussian 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

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“…are referred to as change points. The goal of change point detection (CPD) is to estimate these change points without any prior knowledge on the number and the locations of the change points [30]. The piecewise WSS assumption is not a strict prerequisite for the algorithm to work, but it does accurately summarize the kind of change points our proposed algorithm will be able to detect.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The nominal approach for identifying change points would then be to determine all local maxima and label each local maximum of which the height exceeds a user-defined detection threshold τ as a change point [26], [34]. However, given a window size N , the width of this peak will theoretically be 2N − 1 time stamps, making it likely that noise will cause multiple detection alarms for each ground-truth change point [30]. Our second objective is to mitigate the impact of this issue.…”

Section: Problem Formulationmentioning

confidence: 99%

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

De Ryck,

De Vos,

Bertrand

2020

Preprint

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Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal and suffer from a high false alarm rate. To address these issues, we employ an autoencoderbased methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD. The result is a flexible method that allows the user to indicate whether change points should be sought in the time domain, frequency domain or both. Detectable change points include abrupt changes in the slope, mean, variance, autocorrelation function and frequency spectrum. We demonstrate that our proposed method is consistently highly competitive or superior to baseline methods on diverse simulated and real-life benchmark data sets. Finally, we mitigate the issue of false detection alarms through the use of a postprocessing procedure that combines a matched filter and a newly proposed change point score. We show that this combination drastically improves the performance of our method as well as all baseline methods.

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On Matched Filtering for Statistical Change Point Detection

Cited by 8 publications

References 42 publications

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Dynamical Wasserstein Barycenters for Time-series Modeling

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

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