@inproceedings{f4895463f15c494e8b95e869a6f9993b,
title = "Constrained maximum likelihood based efficient dictionary learning for fMRI analysis",
abstract = "A principal component analysis (PCA) based dictionary initialization approach accompanied by a computationally efficient dictionary learning algorithm for statistical analysis of functional magnetic resonance imaging (fMRI) is proposed. It replaces a singular value decomposition (SVD) computation with an approximate solution to obtain a local minima for a given initial dictionary. The K-SVD has been recently used to develop a data-driven sparse general linear model (GLM) framework for fMRI analysis solely based on the sparsity of signals. However, the K-SVD algorithm is computationally demanding and may require many iterations to converge. Replacing SVD with an approximate solution for the dictionary update combined with an optimal dictionary initialization, the desired results for a sparse GLM can be improved and achieved in few iterations.",
keywords = "EDL, EK-SVD, FMRI, K-SVD, MOD",
author = "Khalid, {Muhammad Usman} and Seghouane, {Abd Krim}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",
year = "2014",
month = jul,
day = "29",
language = "English",
series = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "45--48",
booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
address = "United States",
}