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What is Interpretable Kernel Dimensionality Reduction about?

This document presents a new algorithm called the interpretable kernel dimensionality reduction (I-KDR) algorithm. I-KDR maps data from a feature space to a lower dimensional space where classes are more condensed with less overlap. It does this by creating dimensions based on local contributions from data samples, making the dimensions easier to interpret by class labels. I-KDR also efficiently combines dimensionality reduction with feature selection to select the most relevant original features for the di

Author

Daniel Garcia Murillo

Language

EN