tapkee 1.3.2

C++ library for dimensionality reduction

Tapkee

Python bindings for the Tapkee C++ dimensionality reduction library.

Tapkee provides efficient implementations of over 20 dimensionality reduction methods with a focus on spectral techniques. It leverages Eigen for linear algebra and ARPACK for large-scale eigenproblems.

Installation

pip install tapkee

Usage

import numpy as np
import tapkee

data = np.random.randn(3, 100)  # 3 features, 100 samples
embedding = tapkee.embed(data, method="isomap", num_neighbors=15)

Supported methods

• Spectral: Isomap, Landmark Isomap, Diffusion Maps, Laplacian Eigenmaps
• Local: LLE, Kernel LLE, Hessian LLE, LTSA, Linear LTSA, LPP, NPE
• Linear: PCA, Kernel PCA, Factor Analysis, Random Projection
• Modern: t-SNE, Barnes-Hut-SNE, Stochastic Proximity Embedding
• Other: MDS, Landmark MDS, Manifold Sculpting

Links

• Documentation
• Source code
• Issue tracker

Citation

If you use Tapkee in a publication, please cite:

Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia. Tapkee: An Efficient Dimension Reduction Library. Journal of Machine Learning Research, 14: 2355-2359, 2013.

License

BSD 3-Clause