Non-negative Matrix Factorization and its Application to fMRI

 
When?
Thursday 3 March 2011, 16:00 to 17:00
Where?
39BB02
Open to:
Students, Staff
Speaker:
Mrs Saideh Ferdowsi

Non-negative matrix factorization (NMF) has been widely used for analyzing multivariate data. NMF is a method which creates a low rank approximation for positive data matrix and because of non-negativity constraint it has found interesting applications in image processing where he data is inherently positive.  Functional Magnetic Resonance Imaging (fMRI) is an imaging technique which provides useful anatomical and functional information of brain.  Analyzing data provided by the fMRI helps to investigate brain function.  

In this talk, we first give a brief introduction about different algorithms for fMRI analysis. Then, the application of Non-negative matrix factorization to fMRI data and our proposed algorithm for this purpose will be discussed and its superiority to other data decomposition techniques such as BSS will be emphasised for such data.

Date:
Thursday 3 March 2011
Time:

16:00 to 17:00


Where?
39BB02
Open to:
Students, Staff
Speaker:
Mrs Saideh Ferdowsi

Page Owner: sl0022
Page Created: Wednesday 18 January 2012 19:07:33 by sl0022
Last Modified: Wednesday 18 January 2012 19:10:54 by sl0022
Expiry Date: Monday 28 May 2012 15:48:10
Assembly date: Tue Mar 26 19:38:45 GMT 2013
Content ID: 71873
Revision: 1
Community: 1028