Clustering and Dimensionality Reduction Un Suprevised Machine Learning - Search Videos

Randomized Dimensionality Reduction in Machine Learning

Randomized Dimensionality Reduction in Machine Learning

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. Opens in a new tab

Cluster Analysis

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