New efficient algorithms for multiple change-point detection with reproducing kernels

Célisse, Alain; Marot, Guillemette; Pierre-Jean, Morgane; Rigaill, Guillem

doi:10.1016/j.csda.2018.07.002

Cited by 48 publications

(40 citation statements)

References 42 publications

(131 reference statements)

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“…We establish three metrics,

and

, to measure the performance of the three methods for MCPD. As false change-points are inevitable in the presence of noise [ 39 ], the estimated change-points do not correspond one-to-one with the true change-points. In this experiment, for a true change-point

, the estimated change-point closest to it is selected as its estimation, denoted as

.…”

Section: Resultsmentioning

confidence: 99%

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution

Shen

Zhang

et al. 2020

Entropy

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Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.

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“…We establish three metrics,

and

, the estimated change-point closest to it is selected as its estimation, denoted as

.…”

Section: Resultsmentioning

confidence: 99%

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution

Shen

Zhang

et al. 2020

Entropy

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“…Hence it is an appropriate method to identify the boundaries of fractured zones in one‐dimensional (1D) profile based on well logs. To reduce the computational complexity and perform the efficient boundary detection with multi‐logs, the window‐sliding algorithm based on the radial basis function (RBF) kernel is implemented in this study (Kavzoglu and Colkesen, 2009; Arlot et al ., 2012; Celisse et al ., 2018; Truong et al ., 2020). A new coefficient named discrepancy measure is introduced here to evaluate how different between two subsections.…”

Section: Methodsmentioning

confidence: 99%

Deep learning assisted well log inversion for fracture identification

Miao

Li²,

et al. 2020

Geophysical Prospecting

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Manual fracture identification methods based on cores and image logging pseudopictures are limited by the expense and the amount of data. In this paper, we propose an integrated workflow, which takes the fracture identification as an end-to-end project, to combine the boundary detection and the deep learning classification to recognize fractured zones with accurate locations and reasonable thickness. We first apply the discrete wavelet transform algorithm and a boundary detection method named changing point detection to enhance the fracture sensibility of acoustic logs and segment the whole logging interval into non-overlapping subsections by estimating boundaries. The deep neural network based auto-encoders and the convolutional neural network classifier are then implemented to extract the hidden information from logs and categorize the subsections as the fractured or non-fractured zones. To validate the feasibility of this workflow, we apply it to the logging data from a real well. Compare with the benchmarks provided by the support vector machine , random forest and Adaboost model, the one-dimensional well profile predicted by the proposed changing point detection-deep learning classifier is more consistent with the manual identification result.

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“…Remark 2 (Computing the cost function). Thanks to the well-known "kernel trick", the explicit computation of the mapped data samples φ(y t ) is not required to calculate the cost function value [99]. Indeed, after simple algebraic manipulations, c kernel (y a..b ) can be rewritten as follows:…”

Section: Kernel-based Detectionmentioning

confidence: 99%

“…In addition, kernel change point detection was experimentally shown to be competitive in many different settings, in an unsupervised manner and with very few parameters to manually calibrate. For instance, the cost function c kernel was applied on the Brain-Computer Interface (BCI) data set [96], on a video time series segmentation task [103], DNA sequences [99] and emotion recognition [104]. • The polynomial kernel k(x, y) = ( x|y + C) deg with x, y ∈ R d , and C and deg are parameters.…”

Section: Kernel-based Detectionmentioning

confidence: 99%

Selective review of offline change point detection methods

2020

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This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures.

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New efficient algorithms for multiple change-point detection with reproducing kernels

Cited by 48 publications

References 42 publications

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution

Deep learning assisted well log inversion for fracture identification

Selective review of offline change point detection methods

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