Independent Component Analysis
published: Feb. 25, 2007, recorded: January 2005, views: 73221
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Watch videos: (click on thumbnail to launch)
In independent component analysis (ICA), the purpose is to linearly decompose a multidimensional data vector into components that are as statistically independent as possible. For nongaussian random vectors, this decomposition is not equivalent to decorrelation as is done by principal component analysis, but something considerably more sophisticated. ICA allows one to separate nongaussian source signals from their linear mixtures 'blindly', i.e. using no other information than the congaussianity of the source signals. ICA can also be used to extract features from image and sound signals according to the principle of redundancy reduction that has its origins in the neurosciences. In my talks I will review the basic theory and theoretical background of ICA together with some recent theoretical developments.
Download slides: mlss05au_hyvarinen_ica_01.pdf (5.2 MB)
Download slides: mlss05au_hyvarinen_ica_02.pdf (2.2 MB)
Download slides: mlss05au_hyvarinen_ica_03.pdf (97.5 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !