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Robust cartogram visualization of outliers in manifold learning

Author
Tosi, A.; Vellido, A.
Type of activity
Presentation of work at congresses
Name of edition
21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Date of publication
2013
Presentation's date
2013-04-24
Book of congress proceedings
ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24-26 April 2013
First page
555
Last page
560
Repository
http://hdl.handle.net/2117/23279 Open in new window
URL
https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2013 Open in new window
Abstract
Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case in data density estimation approaches. Manifold learning techniques provide low-dimensional data representations, often oriented towards visualization. The visualization provided by density estimation manifold learning methods can be compromised by the presence of outliers. Recently, a cartogram-based re...
Citation
Tosi, A.; Vellido, A. Robust cartogram visualization of outliers in manifold learning. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24-26 April 2013". Bruges: 2013, p. 555-560.
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants

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