M
Michel Verleysen
Researcher at Université catholique de Louvain
Publications - 446
Citations - 15207
Michel Verleysen is an academic researcher from Université catholique de Louvain. The author has contributed to research in topics: Feature selection & Artificial neural network. The author has an hindex of 54, co-authored 443 publications receiving 13693 citations. Previous affiliations of Michel Verleysen include Carlos III Health Institute & Katholieke Universiteit Leuven.
Papers
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Journal ArticleDOI
Classification in the Presence of Label Noise: A Survey
Benoît Frénay,Michel Verleysen +1 more
TL;DR: In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
Book
Nonlinear Dimensionality Reduction
John Aldo Lee,Michel Verleysen +1 more
TL;DR: The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction, which encompasses many of the recently developed methods.
Journal ArticleDOI
Unique in the Crowd: The privacy bounds of human mobility
Yves-Alexandre de Montjoye,Yves-Alexandre de Montjoye,César A. Hidalgo,César A. Hidalgo,César A. Hidalgo,Michel Verleysen,Vincent D. Blondel,Vincent D. Blondel +7 more
TL;DR: It is found that in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals.
Book ChapterDOI
The curse of dimensionality in data mining and time series prediction
Michel Verleysen,Damien François +1 more
TL;DR: This papers presents alternative distance measures and kernels, together with geometrical methods to decrease the dimension of the space, applied to a typical time series prediction example.
Nonlinear dimensionality reduction
Michel Verleysen,John Aldo Lee +1 more
TL;DR: In this paper, the authors describe existing and advanced methods to reduce the dimensionality of numerical databases and compare them with each other with the help of different illustrative examples, in order to highlight their respective strengths and shortcomings.