Tapkee: An efficient dimension reduction library Journal Article

  


Authors: Lisitsyn, S.; Widmer, C.; Garcia, F. J. I.
Article Title: Tapkee: An efficient dimension reduction library
Abstract: 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. © 2013 Sergey Lisitsyn, Christian Widmer and Fernando J. Iglesias Garcia.
Keywords: dimensionality reduction; machine learning; c++; open source software
Journal Title: Journal of Machine Learning Research
Volume: 14
ISSN: 1532-4435
Publisher: Microtome Publishing  
Date Published: 2013-08-01
Start Page: 2355
End Page: 2359
Language: English
PROVIDER: scopus
DOI/URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84885642698&partnerID=40&md5=8dae9bd6673842a63e903a705eb48060
Notes: --- - "Export Date: 1 November 2013" - "Source: Scopus"