Description:

Tapkee is a C++ template library for dimensionality reduction with
some bias on spectral methods. The Tapkee origins from the code
developed during GSoC 2011 as the part of the Shogun machine learning
toolbox. The project aim is to provide efficient and flexible
standalone library for dimensionality reduction which can be easily
integrated to existing codebases. Tapkee leverages capabilities of
effective Eigen3 linear algebra library and optionally makes use of
the ARPACK eigensolver. The library uses CoverTree and VP-tree
data-structures to compute nearest neighbors. To achieve greater
flexibility we provide a callback interface which decouples dimension
reduction algorithms from the data representation and storage schemes.

Tapkee provides implementations of the following dimension reduction
methods:

* Locally Linear Embedding and Kernel Locally Linear Embedding
(LLE/KLLE)
* Neighborhood Preserving Embedding (NPE)
* Local Tangent Space Alignment (LTSA)
* Linear Local Tangent Space Alignment (LLTSA)
* Hessian Locally Linear Embedding (HLLE)
* Laplacian eigenmaps
* Locality Preserving Projections
* Diffusion map
* Isomap and landmark Isomap
* Multidimensional scaling and landmark Multidimensional scaling
(MDS/lMDS)
* Stochastic Proximity Embedding (SPE)
* PCA and randomized PCA
* Kernel PCA (kPCA)
* Random projection
* Factor analysis
* t-SNE
* Barnes-Hut-SNE