Randomization - Speeding Up Huge Problems

Explore data reduction techniques for analyzing large datasets efficiently in this 47-minute webinar. Learn about two common randomization methods: randomized sub-sampling and local-rank approximations. Compare their applications in PCA and sparse-PCA, examining computational benefits and potential drawbacks on accuracy. Discover strategies for handling immense data tables, particularly in the context of hyperspectral image analysis with millions of pixels. Gain insights into speeding up huge problems without sacrificing crucial analytical information, presented by José Amigo Rubio.