The Experts below are selected from a list of 233472 Experts worldwide ranked by ideXlab platform
Masashi Sugiyama - One of the best experts on this subject based on the ideXlab platform.
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change Point Detection in time series data by relative density ratio estimation
Neural Networks, 2013Co-Authors: Makoto Yamada, Nigel Collier, Masashi SugiyamaAbstract:The objective of change-Point Detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-Point Detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.
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change Point Detection in time series data by relative density ratio estimation
SSPR'12 SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural Syntactic and Statistical Pattern Recognition, 2012Co-Authors: Makoto Yamada, Nigel Collier, Masashi SugiyamaAbstract:The objective of change-Point Detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-Point Detection algorithm that is based on non-parametric divergence estimation between two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on real-world human-activity sensing, speech, and Twitter datasets, we demonstrate the usefulness of the proposed method.
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sequential change Point Detection based on direct density ratio estimation
Statistical Analysis and Data Mining, 2012Co-Authors: Yoshinobu Kawahara, Masashi SugiyamaAbstract:Change-Point Detection is the problem of discovering time Points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel nonparametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid nonparametric density estimation, which is known to be a difficult problem. We provide a change-Point Detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real-world datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011 © 2012 Wiley Periodicals, Inc.
A Ustyuzhanin - One of the best experts on this subject based on the ideXlab platform.
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generalization of change Point Detection in time series data based on direct density ratio estimation
Journal of Computational Science, 2021Co-Authors: Mikhail Hushchyn, A UstyuzhaninAbstract:Abstract The goal of the change-Point Detection is to discover changes of time series distribution. One of the state of the art approaches of change-Point Detection is based on direct density ratio estimation. In this work, we show how existing algorithms can be generalized using various binary classification and regression models. In particular, we show that the Gradient Boosting over Decision Trees and Neural Networks can be used for this purpose. The algorithms are tested on several synthetic and real-world datasets. The results show that the proposed methods outperform classical RuLSIF algorithm. Discussion of cases where the proposed algorithms have advantages over existing methods is also provided.
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generalization of change Point Detection in time series data based on direct density ratio estimation
arXiv: Learning, 2020Co-Authors: Mikhail Hushchyn, A UstyuzhaninAbstract:The goal of the change-Point Detection is to discover changes of time series distribution. One of the state of the art approaches of the change-Point Detection are based on direct density ratio estimation. In this work we show how existing algorithms can be generalized using various binary classification and regression models. In particular, we show that the Gradient Boosting over Decision Trees and Neural Networks can be used for this purpose. The algorithms are tested on several synthetic and real-world datasets. The results show that the proposed methods outperform classical RuLSIF algorithm. Discussion of cases where the proposed algorithms have advantages over existing methods are also provided.
Maja Pantic - One of the best experts on this subject based on the ideXlab platform.
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facial Point Detection using boosted regression and graph models
Computer Vision and Pattern Recognition, 2010Co-Authors: Michel Valstar, Brais Martinez, Xavier Binefa, Maja PanticAbstract:Finding fiducial facial Points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-feature-based facial expression analysis, and methods that use appearance-based features extracted at fiducial facial Point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a Point's location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial Points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a Point and the positions of these Points, which makes Detection of the Points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed Point Detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art Point detectors.
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fully automatic facial feature Point Detection using gabor feature based boosted classifiers
Systems Man and Cybernetics, 2005Co-Authors: Danijela Vukadinovic, Maja PanticAbstract:Locating facial feature Points in images of faces is an important stage for numerous facial image interpretation tasks. In this paper we present a method for fully automatic Detection of 20 facial feature Points in images of expressionless faces using Gabor feature based boosted classifiers. The method adopts fast and robust face Detection algorithm, which represents an adapted version of the original Viola-Jones face detector. The detected face region is then divided into 20 relevant regions of interest, each of which is examined further to predict the location of the facial feature Points. The proposed facial feature Point Detection method uses individual feature patch templates to detect Points in the relevant region of interest. These feature models are GentleBoost templates built from both gray level intensities and Gabor wavelet features. When tested on the Cohn-Kanade database, the method has achieved average recognition rates of 93%.
Kilhoum Park - One of the best experts on this subject based on the ideXlab platform.
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efficient fiducial Point Detection of ecg qrs complex based on polygonal approximation
Sensors, 2018Co-Authors: Yoosoo Jeong, Daejin Park, Kilhoum ParkAbstract:Electrocardiogram signal analysis is based on detecting a fiducial Point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial Point Detection. Detecting the onset and offset fiducial Points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper’s fiducial Point Detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial Point Detection and emphasizes these sample’s feature values to enable reliable Detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as −4.02±7.99 ms and −5.45±8.04 ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial Point Detection results for various types of QRS complexes.
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singular Point Detection by shape analysis of directional fields in fingerprints
Pattern Recognition, 2006Co-Authors: Chulhyun Park, Joonjae Lee, Mark J T Smith, Kilhoum ParkAbstract:This paper presents a new fingerprint singular Point Detection method that is type-distinguishable and applicable to various fingerprint images regardless of their resolutions. The proposed method detects singular Points by analyzing the shapes of the local directional fields of a fingerprint image. Using the predefined rules, all types of singular Points (upper core, lower core, and delta Points) can be extracted accurately and delineated in terms of the type of singular Points. In case of arch-type fingerprints there exists no singular Point, but reference Points for arch-type fingerprints are required to be detected for registration. Therefore, we propose a new reference Point Detection method for arch-type fingerprints as well. The result of the experiments on the two public databases (FVC2000 2a, FVC2002 2a) with different resolutions demonstrates that the proposed method has high accuracy in locating each types of singular Points and detecting the reference Points of arch-type fingerprints without regard to their image resolutions.
Makoto Yamada - One of the best experts on this subject based on the ideXlab platform.
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change Point Detection in time series data by relative density ratio estimation
Neural Networks, 2013Co-Authors: Makoto Yamada, Nigel Collier, Masashi SugiyamaAbstract:The objective of change-Point Detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-Point Detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.
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change Point Detection in time series data by relative density ratio estimation
SSPR'12 SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural Syntactic and Statistical Pattern Recognition, 2012Co-Authors: Makoto Yamada, Nigel Collier, Masashi SugiyamaAbstract:The objective of change-Point Detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-Point Detection algorithm that is based on non-parametric divergence estimation between two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on real-world human-activity sensing, speech, and Twitter datasets, we demonstrate the usefulness of the proposed method.
