Unsupervised dimensionality reduction: the challenge of big data visualisation

Apr, 2015

Dimensionality reduction is an unsupervised task that allows high-dimensional data to be processed or visualised in lower-dimensional spaces. This tutorial reviews the basic principles of dimensionality reduction and discusses some of the approaches that were published over the past years from the perspective of their application to big data. The tutorial ends with a short review of papers about dimensionality reduction in these proceedings, as well as some perspectives for the near future

Link

@incollection{ESANNeditorial2015,
author = {John Aldo Lee and Kerstin Bunte},
title = {Unsupervised dimensionality reduction: the challenge of big data visualisation},
editor = {M. Verleysen},
booktitle = {ESANN 2015 - 23rd European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
publisher = {D-side},
address = {Bruges},
year = {2015},
pages = {487--494},
chapter1 = {10},
month = {Apr},
isbn = {978-2-87587-014-8},
url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-16.pdf},
abstract = {Dimensionality reduction is an unsupervised task that allows high-dimensional data to be processed or visualised in lower-dimensional spaces.  This tutorial reviews the basic principles of dimensionality reduction and discusses some of the approaches that were published over the past years from the  perspective of their application to big data.  The tutorial ends with a short review of papers about dimensionality reduction in these proceedings,  as well as some perspectives for the near future},
note = {Special Session at ESANN 2015 : Unsupervised dimensionality reduction: the challenge of big data visualisation ; Conference date: 22-04-2015 Through 24-04-2015"},
}