Randomized Dimensionality Reduction in Machine Learning

Опубликовано 12 августа 2016, 2:04

We show how certain random projections and random sampling methods can be used to design efficient dimensionality reduction techniques for two popular machine learning problems: (i) K-means Clustering, and (ii) Canonical Correlation Analysis. In both cases, we argue that randomized dimensionality reduction is provably efficient.