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Tapkee: An Efficient Dimension Reduction Library
KTH, School of Computer Science and Communication (CSC).
2013 (English)In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 14, 2355-2359 p.Article in journal (Refereed) Published
Abstract [en]

We present Tapkee, a C++ template library that provides efficient implementations of more than 20 widely used dimensionality reduction techniques ranging from Locally Linear Embedding (Roweis and Saul, 2000) and Isomap (de Silva and Tenenbaum, 2002) to the recently introduced Barnes-Hut-SNE (van der Maaten, 2013). Our library was designed with a focus on performance and flexibility. For performance, we combine efficient multi-core algorithms, modern data structures and state-of-the-art low-level libraries. To achieve flexibility, we designed a clean interface for applying methods to user data and provide a callback API that facilitates integration with the library. The library is freely available as open-source software and is distributed under the permissive BSD 3-clause license. We encourage the integration of Tapkee into other open-source toolboxes and libraries. For example, Tapkee has been integrated into the codebase of the Shogun toolbox (Sonnenburg et al., 2010), giving us access to a rich set of kernels, distance measures and bindings to common programming languages including Python, Octave, Matlab, R, Java, C#, Ruby, Perl and Lua. Source code, examples and documentation are available at

Place, publisher, year, edition, pages
2013. Vol. 14, 2355-2359 p.
Keyword [en]
dimensionality reduction, machine learning, C plus, open source software
National Category
Computer Science
URN: urn:nbn:se:kth:diva-131732ISI: 000324799600006ScopusID: 2-s2.0-84885642698OAI: diva2:656830

QC 20131017

Available from: 2013-10-17 Created: 2013-10-17 Last updated: 2013-10-17Bibliographically approved

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Garcia, Fernando J. Iglesias
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