Non negative matrix factorization, sparse coding & dictionary learning techniques on fMRI images

Abstract

Matrix factorization techniques have proved to be successful for the Source Separation (SS) of different types of data. Recent developments in Matrix factorization techniques have led to sparse representation of signals using learned dictionaries. In this research we have applied Alternating Least Square Non-Negative Matrix Factorization (ALSNMF) and Dictionary Learning (DL) technique for sparse representation to simulated and real Functional Magnetic Resonance Images (fMRI) to extract corresponding sources and time courses. These different techniques with varying the rank values/dictionary sizes for ALSNMF/DL respectively for SS of fMRI have been analyzed and conclusion has been made in terms of quality and efficiency of the SS of fMRI with respect to the variation of rank values/dictionary sizes. ALSNMF method is the best among both applied methods in terms of fast convergence and performance for high rank values and can extract best time courses and Sources simultaneously. K-SVD algorithm performed well particularly for real fMRI datasets. However, for small dictionary size, sources are extracted well with degraded time course extraction and vice versa for large dictionary size.