Unsupervised dimensionality reduction: the challenge of big data visulisation
(2015) ESANN 2015 - 23rd Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning — ISBN: [978-2-87587-014-8], p. 487-494, published
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- 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.
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Citations
Lee, J., & Bunte, K. (2015). Unsupervised dimensionality reduction: the challenge of big data visulisation. In ESANN 2015 - 23rd Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. p. 487-494). D-side. https://hdl.handle.net/2078.5/187871