Abstract
We address novel developments in the context of dimensionality reduction for data visualization. We consider nonlinear non-parametric techniques such as t-distributed stochastic neighbor embedding and discuss the difficulties which are encountered if large data sets are dealt with, in contrast to parametric approaches such as the self-organizing map. We focus on the following topics, which arise in this context: (i) how can dimensionality reduction be realized efficiently in at most linear time, (ii) how can nonparametric approaches be extended to provide an explicit mapping, (iii) how can techniques be extended to incorporate auxiliary information as provided by class labeling?
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Hammer, B., Gisbrecht, A., Schulz, A. (2013). How to Visualize Large Data Sets?. In: Estévez, P., PrÃncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_1
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DOI: https://doi.org/10.1007/978-3-642-35230-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35229-4
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