University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Random Projections

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Random Projections

Download to your calendar using vCal

If you have a question about this talk, please contact Konstantina Palla .

A fundamental result of Johnson and Lindenstrauss states that one may randomly project a collection of data points into a lower dimensional space while preserving pairwise point distances. How is this possible? Recent developments have gone even further: non-linear randomised projections can be used to approximate kernel machines and scale them to datasets with millions of features and samples. In this talk we will explore the theoretical aspects of the great Random Projection method, and perform live demos to demonstrate its effectiveness.

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

History History Faculty Room 5 The Eastern Counties Branch of The Welding Institute

Other talks

Title to be confirmed Studying viruses to understand what makes pandemic viruses special and how innate immunity works Title to be confirmed Group Work Optimal metric and approximation of free boundary minimal surfaces Genomic Medicine CPC
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity