Kernel method for out-of-sample extension
Project description
kerneloose
Python implementation of the kernel method for out-of-sample extension (OOSE) of dimensionality reduction techniques.
Based on "Parametric nonlinear dimensionality reduction using kernel t-SNE" by Gisbrecht, Schulz, and Hammer.
The kernel method is particularly useful for projection techniques that are computationally expensive and/or have non-convex objective functions, such as t-SNE.
Installation
pip install kerneloose
Usage example
The syntax follows scikit learn conventions.
Assume 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:
kernel_oose.transform(new_data)
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')
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