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http://tapkee./
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.
supporting:
Tapkee provides implementations of the following dimension
reduction methods (urls to descriptions provided):
- 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
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