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Detecting changes of mean in multidimensional normal sequences with applications to literature and geology

COMPUTATIONAL STATISTICS 1996年第3期11卷 211-221页

作者： Gupta, AK Chen, J UNIV MISSOURI DEPT MATH & STATKANSAS CITYMO 64110

In this paper, the Schwarz information criterion together with the binary segmentation procedure has been applied to detect change points in a set of geological data and the changes in the frequencies of pronouns in the plays of Shakespeare. The emphasis here is on the exploratory data analysis rather than the theoretical statistical investigation.

关键词： multiple change-points multivariate normal binary segmentation AIC SIC

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Camouflaged Object Detection via Complementary Information-Selected Network Based on Visual and Semantic Separation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第11期20卷 12871-12881页

作者： Yin, Chao Yang, Kequan Li, Jide Li, Xiaoqiang Wu, Yifan Shanghai Univ Comp Engn & Sci Dept Shanghai 200444 Peoples R China

Camouflaged object detection (COD) is a promising yet challenging task that aims to segment objects concealed within intricate surroundings, a capability crucial for modern industrial applications. Current COD methods primarily focus on the direct fusion of high-level and low-level information, without considering their differences and inconsistencies. Consequently, accurately segmenting highly camouflaged objects in challenging scenarios presents a considerable problem. To mitigate this concern, we propose a novel framework called visual and semantic separation network (VSSNet), which separately extracts low-level visual and high-level semantic cues and adaptively combines them for accurate predictions. Specifically, it features the information extractor module for capturing dimension-aware visual or semantic information from various perspectives. The complementary information-selected module leverages the complementary nature of visual and semantic information for adaptive selection and fusion. In addition, the region disparity weighting strategy encourages the model to prioritize the boundaries of highly camouflaged and difficult-to-predict objects. Experimental results on benchmark datasets show the VSSNet significantly outperforms State-of-the-Art COD approaches without data augmentations and multiscale training techniques. Furthermore, our method demonstrates satisfactory cross-domain generalization performance in real-world industrial environments.

关键词： Feature extraction Semantics Visualization Task analysis Image segmentation Object detection Data mining binary segmentation camouflaged object detection (COD) deep learning

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Development of an image binarization software tool for net occlusion estimations

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AQUACULTURAL ENGINEERING 2024年 107卷

作者： Cappaert, R. Yang, W. Ross, D. J. Johnston, C. Macleod, C. White, C. A. Univ Tasmania Inst Marine & Antarctic Studies Taroona Private Bag 49 Hobart Tas 7001 Australia Univ Tasmania Sch Informat & Commun Technol Private Bag 51 Hobart Tas 7001 Australia Tassal 1-5 Franklin Whrf Hobart Tas 7000 Australia Blue Econ Cooperat Res Ctr Launceston Tas Australia

Marine biofouling poses a set of challenges to the salmon aquaculture industry, where an accumulation of biota on pens can lead to significant net mesh occlusion. This can reduce flow rates, risking oxygen depletion. The industry currently manages this challenge through regular cleaning of nets, with sporadic manual visual estimations of net occlusion an important but time-consuming task. This study developed a simple automated desktop application to more regularly and more robustly quantify pen net occlusion caused by biofouling. This software application pre-processes and binarizes images collected from cameras currently used by the industry into water and non-water pixels. The percentage of net occlusion is then calculated from the binary image. Accurate binarization of representative images was achieved by training a deep learning network on images collected in situ. . The resulting network attained a validation accuracy of 96.4% and a mean test accuracy of 93.5%. From the test images, 98.3% of pixels annotated as non-water and 88.8% of pixels annotated as water were correctly classified by the network. This automated tool has the capacity to better inform industry and create a more efficient cleaning framework based on the needs of individual pens, based on data that can be more readily obtained as compared to manual net inspections.

关键词： Marine biofouling Salmon aquaculture Industry camera system Net occlusion binary segmentation Desktop application

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Bayesian Multiple Change-Points Detection in a Normal Model with Heterogeneous Variances

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COMPUTATIONAL STATISTICS 2021年第2期36卷 1365-1390页

作者： Kang, Sang Gil Lee, Woo Dong Kim, Yongku Sangji Univ Dept Comp & Data Informat Wonju 26339 South Korea Daegu Haany Univ Premajor Cosmet & Pharmaceut Gyongsan 38610 South Korea Kyungpook Natl Univ Dept Stat Daegu 41566 South Korea

This study considers the problem of multiple change-points detection. For this problem, we develop an objective Bayesian multiple change-points detection procedure in a normal model with heterogeneous variances. Our Bayesian procedure is based on a combination of binary segmentation and the idea of the screening and ranking algorithm (Niu and Zhang in Ann Appl Stat 6:1306-1326, 2012). Using the screening and ranking algorithm, we can overcome the drawbacks of binary segmentation, as it cannot detect a small segment of structural change in the middle of a large segment or segments of structural changes with small jump magnitude. We propose a detection procedure based on a Bayesian model selection procedure to address this problem in which no subjective input is considered. We construct intrinsic priors for which the Bayes factors and model selection probabilities are well defined. We find that for large sample sizes, our method based on Bayes factors with intrinsic priors is consistent. Moreover, we compare the behavior of the proposed multiple change-points detection procedure with existing methods through a simulation study and two real data examples.

关键词： Bayes factor binary segmentation Intrinsic prior Multiple change-points Screening and ranking algorithm

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An Adaptive Intrusion Detection Scheme for Cloud Computing

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第4期10卷 53-70页

作者： Ibrahim, Nurudeen Mahmud Zainal, Anazida Univ Teknol Malaysia Johor Baharu Malaysia

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.

关键词： Anomaly Detection Ant Colony Optimization binary segmentation Cloud Security DDoS Machine Learning Stochastic Gradient Descent Time Series

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Optimal covariance change point localization in high dimensions

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BERNOULLI 2021年第1期27卷 554-575页

作者： Wang, Daren Yu, Yi Rinaldo, Alessandro Univ Chicago Dept Stat 5747 S Ellis Ave Chicago IL 60637 USA Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England Carnegie Mellon Univ Dept Stat & Data Sci Pittsburgh PA 15213 USA

We study the problem of change point localization for covariance matrices in high dimensions. We assume that we observe a sequence of independent and centered p-dimensional sub-Gaussian random vectors whose covariance matrices are piecewise constant, and only change at unknown times. We are concerned with the localization task of estimating the positions of the change points. In our analysis, we allow for all the model parameters to change with the sample size n, including the dimension p, the minimal spacing between consecutive change points Delta, the maximal Orlicz-psi(2) norm B of the sample points and the magnitude kappa of the smallest distributional change, defined as the minimal operator norm of the difference between the covariance matrix at a change point and the covariance matrix at the previous time point. We introduce two procedures, one based on the binary segmentation algorithm and the other on its popular extension known as wild binary segmentation, and demonstrate that, under suitable conditions, both procedures can consistently estimate the change points. In particular, our second algorithm, called Wild binary segmentation through Independent Projection (WBSIP), delivers a localization error of order B-4 kappa(-2)log(n), which is shown to be minimax rate optimal, save, possibly, for the log(n) term. WBSIP requires the model parameters to satisfy the scaling Delta kappa(2) greater than or similar to pB(4) log(1+xi), for any xi > 0, which we demonstrate to be essentially necessary, in the sense that no algorithm can guarantee consistent localization if Delta kappa(2) less than or similar to pB(4). This result reveals an interesting phase transition effect separating parameter combinations for which the localization task is feasible from the ones for which it is not.

关键词： binary segmentation change point detection high-dimensional covariance testing independent projection minimax optimal wild binary segmentation

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Segmenting time series via self-normalisation

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2022年第5期84卷 1699-1725页

作者： Zhao, Zifeng Jiang, Feiyu Shao, Xiaofeng Univ Notre Dame Mendoza Coll Business Notre Dame IN 46556 USA Fudan Univ Dept Stat & Data Sci Shanghai Peoples R China Univ Illinois Dept Stat Champaign IL USA

We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully non-parametric, robust to temporal dependence and avoids the demanding consistent estimation of long-run variance. One salient and distinct feature of the proposed method is its versatility, where it allows change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the core of our method, we couple the self-normalisation- (SN) based tests with a novel nested local-window segmentation algorithm, which seems new in the growing literature of change-point analysis. Due to the presence of an inconsistent long-run variance estimator in the SN test, non-standard theoretical arguments are further developed to derive the consistency and convergence rate of the proposed SN-based change-point detection method. Extensive numerical experiments and relevant real data analysis are conducted to illustrate the effectiveness and broad applicability of our proposed method in comparison with state-of-the-art approaches in the literature.

关键词： binary segmentation change-point detection long-run variance scanning studentisation temporal dependence

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Most recent changepoint detection in censored panel data

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COMPUTATIONAL STATISTICS 2021年第1期36卷 515-540页

作者： Siddiqa, Hajra Ali, Sajid Shah, Ismail Quaid I Azam Univ Dept Stat Islamabad 45320 Pakistan

This study aims to detect the most recent changepoint in censored panel data by ignoring dependence within and between segments as well as taking into account the serial autocorrelation. A comparison of different methods to detect the most recent changepoint for censored data is presented. Different censoring rates such as 20%, 50%, and 90% in the case of right and left censoring while (10%, 10%), (25%, 25%) and (40%, 50%) for interval censoring are considered. Further, we use most recent changepoint (MRC), double cumulative sum binary segmentation, non parametric changepoint detection (ECP), multiple changepoints in multivariate time series, analyzing each series in the panel independently, and analyzing aggregated data (AGG) methods. It is observed that different censoring rates have a significant effect on the detection of changepoints in high dimensional data. It is also noticed that the MRC method outperforms the competing methods considered in this study. In addition to investigating the impact of penalties, the performance of MRC and AGG methods is also compared using water quality data of the Niagara River. Also, a data set related to survival time of stroke patients is also a part of this study. An R package "cpcens" is available in comprehensive R archive network to replicate the results of this article.

关键词： Change point Panel censored data High dimensional data CUSUM binary segmentation Cost function

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Univariate mean change point detection: Penalization, CUSUM and optimality

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ELECTRONIC JOURNAL OF STATISTICS 2020年第1期14卷 1917-1961页

作者： Wang, Daren Yu, Yi Rinaldo, Alessandro Univ Chicago Dept Stat 5747 S Ellis AveJones 120A Chicago IL 60637 USA Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England Carnegie Mellon Univ Dept Stat & Data Sci Pittsburgh PA 15213 USA

The problem of univariate mean change point detection and localization based on a sequence of n independent observations with piecewise constant means has been intensively studied for more than half century, and serves as a blueprint for change point problems in more complex settings. We provide a complete characterization of this classical problem in a general framework in which the upper bound sigma(2) on the noise variance, the minimal spacing Delta between two consecutive change points and the minimal magnitude kappa of the changes, are allowed to vary with n. We first show that consistent localization of the change points is impossible in the low signal-to-noise ratio regime kappa root Delta/sigma <= root log(n). In contrast, when kappa root Delta/sigma diverges with n at the rate of at least root log(n), we demonstrate that two computationally-efficient change point estimators, one based on the solution to an to-penalized least squares problem and the other on the popular wild binary segmentation algorithm, are both consistent and achieve a localization rate of the order sigma(2)/kappa(2) log(n). We further show that such rate is minimax optimal, up to a log(n) term.

关键词： Change point detection minimax optimality l(0)-penalization CUSUM statistics binary segmentation

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Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence

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STATISTICAL PAPERS 2024年第4期65卷 2327-2359页

作者： Duker, Marie-Christine Jeong, Seok-Oh Lee, Taewook Baek, Changryong Cornell Univ Dept Stat & Data Sci Ithaca NY 14850 USA Hankuk Univ Foreign Studies Dept Stat 81 Oedae Ro Yongin 449791 Kyunggi Do South Korea Sungkyunkwan Univ Dept Stat 25-2 Sungkyunkwan Ro Seoul 03063 South Korea

We consider the detection of multiple change-points in a high-dimensional time series exhibiting both cross-sectional and temporal dependence. Several test statistics based on the celebrated CUSUM statistic are used and discussed. In particular, we propose a novel block wild bootstrap method to address the presence of cross-sectional and temporal dependence. Furthermore, binary segmentation and the moving sum algorithm are considered to detect and locate multiple change-points. We also give some theoretical justifications for the moving sum method. An extensive numerical study provides insights on the performance of the proposed methods. Finally, our proposed procedures are used to analyze financial stock data in Korea.

关键词： Change point analysis High-dimensional time series Block wild bootstrap CUSUM binary segmentation Moving sum algorithm

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