Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
Waffles: A Machine Learning Toolkit
The Journal of Machine Learning Research
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
An Algorithm for the Principal Component Analysis of Large Data Sets
SIAM Journal on Scientific Computing
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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.