Kernel method for out-of-sample extension
Python implementation of the kernel method for out-of-sample extension (OOSE) of dimensionality reduction techniques.
The kernel method is particularly useful for projection techniques that are computationally expensive and/or have non-convex objective functions, such as t-SNE.
pip install kerneloose
The syntax follows scikit learn conventions.
hd_data is a numpy array containing high-dimensional data, and an array
ld_data of equal length but lower dimension was obtained by some projection technique.
An OOSE of that projection can be obtained by:
from kerneloose import KernelMap kernel_oose = KernelMap() kernel_oose.fit(hd_data, ld_data)
The mapping can be applied to
new_data (with same dimensionality as
ld_data) simply by:
Parameters of the calculated OOSE mapping can be saved and loaded for later use:
kernel_oose.save('some/file/name') resume_later = KernelMap() resume_later.load('some/file/name')
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size kerneloose-0.0.2-py3-none-any.whl (4.8 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size kerneloose-0.0.2.tar.gz (3.6 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for kerneloose-0.0.2-py3-none-any.whl