Skip to main content

Kernel method for out-of-sample extension

Project description


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.


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(), 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:'some/file/name')

resume_later = KernelMap()

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for kerneloose, version 0.0.2
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

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page