Python implementation of supervised PCA, supervised random projections, and their kernel counterparts.
Supervised Random Pojections (SRP) is the work of Amir-Hossein Karimi, Alexander Wong, and Ali Ghodsi. It is a fast approximation of the Supervised PCA algortithm for dimensionality reduction. It also has a nonlinear version, Kernel SRP (KSRP).
This repository provides a unified implementation of SPCA, KSPCA, SRP and KSRP. They are implemented as scikit-learn transformers, and can therefore be used exactly like scikit-learn's PCA and KPCA. Moreover, SRP and KSRP can be performed using a LigthOn Optical Processing Unit (OPU).
dimreduc.pycontains the implementations of the algorithms;load_data.pycontains utilities to load the datasets used in the original paper (XOR, Spirals, Sonar and Ionosphere);sonar_viz.pyshows how to use this code for visualizing the Sonar dataset.
The Ionosphere and Sonar dataset come from the UCI repository. They are tiny, so I included them in the data folder for convenience.
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