Tapkee: An Efficient Dimension Reduction Library
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
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 http://tapkee.lisitsyn.me.
Place, publisher, year, edition, pages
2013. Vol. 14, 2355-2359 p.
dimensionality reduction, machine learning, C plus, open source software
IdentifiersURN: urn:nbn:se:kth:diva-131732ISI: 000324799600006ScopusID: 2-s2.0-84885642698OAI: oai:DiVA.org:kth-131732DiVA: diva2:656830
QC 201310172013-10-172013-10-172013-10-17Bibliographically approved