On the Role and Impact of the Metaparameters in t-distributed Stochastic Neighbor Embedding

John A. Lee () and Michel Verleysen ()
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John A. Lee: Imagerie Moléculaire et Radiothérapie Expérimentale
Michel Verleysen: Machine Learning Group - DICE

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 337-346 from Springer

Abstract: Abstract Similarity-based embedding is a paradigm that recently gained interest in the field of nonlinear dimensionality reduction. It provides an elegant framework that naturally emphasizes the preservation of the local structure of the data set. An emblematic method in this trend is t-distributed stochastic neighbor embedding (t-SNE), which is acknowledged to be an efficient method in the recent literature. This paper aims at analyzing the reasons of this success, together with the impact of the two metaparameters embedded in the method. Moreover, the paper shows that t-SNE can be interpreted as a distance-preserving method with a specific distance transformation, making the link with existing methods. Experiments on artificial data support the theoretical discussion.

Keywords: similarity-based embedding; dimensionality reduction; nonlinear projection; manifold learning; t-SNE (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_31

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DOI: 10.1007/978-3-7908-2604-3_31

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