Výsledky vyhledávání - "Peter J. Rousseeuw"

Clustering genomic words in human DNA using peaks and trends of distributions

Autor: Peter J. Rousseeuw, Vera Afreixo, Jakob Raymaekers, Ana Tavares, Paula Brito

Publikováno v: Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Advances in Data Analysis and Classification, 14(1), 57-76. Springer Verlag

© 2019, The Author(s). In this work we seek clusters of genomic words in human DNA by studying their inter-word lag distributions. Due to the particularly spiked nature of these histograms, a clustering procedure is proposed that first decomposes ea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f84174dce0fa0a267dbb3bd2f818e01
https://doi.org/10.1007/s11634-019-00362-x

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Fast Robust Correlation for High-Dimensional Data

Autor: Peter J. Rousseeuw, Jakob Raymaekers

Publikováno v: Technometrics, 63(2), 184-198. American Statistical Association

The product moment covariance matrix is a cornerstone of multivariate data analysis, from which one can derive correlations, principal components, Mahalanobis distances and many other results. Unfortunately, the product moment covariance and the corr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c47e814c694372b9129a42d4f1ea3553
https://doi.org/10.1080/00401706.2019.1677270

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A generalized spatial sign covariance matrix

Autor: Peter J. Rousseeuw, Jakob Raymaekers

Publikováno v: Journal of Multivariate Analysis, 171, 94-111. Academic Press Inc.

© 2018 The Authors The well-known spatial sign covariance matrix (SSCM) carries out a radial transform which moves all data points to a sphere, followed by computing the classical covariance matrix of the transformed data. Its popularity stems from

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::792f28cc2d3a19e804b6fdbaa3204aef
https://doi.org/10.1016/j.jmva.2018.11.010

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Transforming variables to central normality

Autor: Peter J. Rousseeuw, Jakob Raymaekers

Publikováno v: Machine Learning. Springer

Many real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more norma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15e9cad41e9d9666a948c38ead8666df
http://arxiv.org/abs/2005.07946

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The minimum regularized covariance determinant estimator

Autor: Kris Boudt, Peter J. Rousseeuw, Steven Vanduffel, Tim Verdonck

Publikováno v: Statistics and Computing, 30(1), 113-128. Springer Netherlands
Statistics and computing
Boudt, K, Rousseeuw, P J, Vanduffel, S & Verdonck, T 2020, ' The minimum regularized covariance determinant estimator ', Statistics and Computing, vol. 30, no. 1, pp. 113-128 . https://doi.org/10.1007/s11222-019-09869-x

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its main dra

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eec60080fc0a78b72abbc514d242ba55
https://research.vu.nl/en/publications/5b338fea-dd6b-4527-b9a8-a3ac4183b6e1

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Discussion of 'The power of monitoring: how to make the most of a contaminated multivariate sample'

Autor: Jakob Raymaekers, Iwein Vranckx, Peter J. Rousseeuw

Publikováno v: Statistical Methods and Applications, 27(4), 589-594. Springer

In this comment on the discussion paper “The power of monitoring: how to make the most of a contaminated multivariate sample” by A. Cerioli, M. Riani, A. Atkinson and A. Corbellini, we describe how the hard rejection property of the MCD method ca

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https://doi.org/10.1007/s10260-018-0425-3

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Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms

Autor: Erich Schubert, Peter J. Rousseeuw

Publikováno v: Information Systems. 101:101804

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also simply referred to as k -medoids clustering. In Euclidean geometry the me

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fab3a0f85704cc65ab4353c71603bfe6
https://doi.org/10.1016/j.is.2021.101804

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Real-time discriminant analysis in the presence of label and measurement noise

Autor: Mia Hubert, Peter J. Rousseeuw, Bart De Ketelaere, Jakob Raymaekers, Iwein Vranckx

Publikováno v: Chemometrics and Intelligent Laboratory Systems, 208. Elsevier Science

Quadratic discriminant analysis (QDA) is a widely used classification technique. Based on a training dataset, each class in the data is characterized by an estimate of its center and shape, which can then be used to assign unseen observations to one

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::983bb240ea30b1c1562e41c93e586417
https://doi.org/10.1016/j.chemolab.2020.104197

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Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms

Autor: Peter J. Rousseeuw, Erich Schubert

Publikováno v: Similarity Search and Applications ISBN: 9783030320461
SISAP

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1192fba9341ff4b487b6a734bb997c22
https://doi.org/10.1007/978-3-030-32047-8_16

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Real-time outlier detection for large datasets by RT-DetMCD

Autor: Jakob Raymaekers, Mia Hubert, Iwein Vranckx, Peter J. Rousseeuw, Bart De Ketelaere

Publikováno v: Chemometrics and Intelligent Laboratory Systems, 199. Elsevier Science

Modern industrial machines can generate gigabytes of data in seconds, frequently pushing the boundaries of available computing power. Together with the time criticality of industrial processing this presents a challenging problem for any data analyti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4c968754ffec4b09d913b8cf72bc74a
https://doi.org/10.1016/j.chemolab.2020.103957

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