Non-negative Matrix Factorization and its Application to fMRI

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.