dc.contributor.author | Wang, Xiulin | |
dc.date.accessioned | 2020-11-18T15:18:05Z | |
dc.date.available | 2020-11-18T15:18:05Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-951-39-8407-6 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/72668 | |
dc.description.abstract | Continuous advancement of brain imaging techniques has witnessed data analysis
methods evolving from matrix component analysis to tensor component analysis,
from individual analysis to group analysis regarding the analysis of brain
data with multi-set/multi-modal, multi-coupling and multi-way characteristics.
Coupled matrix/tensor factorization is robust in merging the advantages of analysis
methods, including multi-way retainability, flexible coupling settings, mild
uniqueness conditions, and applicability of various constraints, which is relatively
difficult for most existing methods. Therefore, this dissertation aims to
develop efficient coupled nonnegative matrix/tensor factorization algorithms,
which can be used for the analysis of brain imaging data at the group level.
First, aiming at constrained group analysis of data from multiple sources,
we design a flexible model of coupled nonnegative matrix factorization with
sparse regularization and adopt alternating direction method of multipliers (ADMM)
for optimization. Then, to reduce the high computational cost of largescale
problems, we propose three efficient coupled nonnegative tensor factorization
algorithms, which are respectively based on fast hierarchical alternating least
squares (fHALS), alternating proximal gradient (APG) and a combination of APG
and low-rank approximation.
Experiments using synthetic and real-world data are conducted to demonstrate
the performances of the proposed algorithms. Specifically, for multi-subject
simulated functional magnetic resonance imaging data, the proposed ADMMbased
algorithm can achieve better performance than its competitors and extract
both common and individual patterns while correcting the disorders of common
patterns. For multi-subject ongoing electroencephalography data, the proposed
fHALS-based algorithm can effectively extract brain activities of interest associated
with the musical stimulus. For multi-subject event-related potential data,
the proposed APG-based algorithms can obtain higher decomposition accuracy
and more robust multi-domain feature extraction stability, and low-rank approximation
can greatly improve computation efficiency without losing the accuracy.
Overall, according to data characteristics , we have developed efficient coupled
nonnegative matrix/tensor decomposition algorithms, which have been successfully
applied to the group analysis of brain imaging data.
Keywords: Brain imaging data, coupled constraint, group analysis, nonnegative
matrix/tensor factorization, sparse regularization | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Jyväskylän yliopisto | |
dc.relation.ispartofseries | JYU Dissertations | |
dc.relation.haspart | <b>Artikkeli I:</b> Wang, Xiulin; Liu, Wenya; Cong, Fengyu; Ristaniemi, Tapani (2020). Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data. In <i>EUSIPCO 2020 : 28th European Signal Processing Conference (pp. 2125-2129). European Signal Processing Conference. IEEE.</i> <a href="https://doi.org/10.23919/Eusipco47968.2020.9287756"target="_blank"> DOI: 10.23919/Eusipco47968.2020.9287756</a> | |
dc.relation.haspart | <b>Artikkeli II:</b> Wang, X., Zhang, C., Ristaniemi, T., & Cong, F. (2019). Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis. In <i>H. Lu, H. Tang, & Z. Wang (Eds.), ISNN 2019 : Advances in Neural Networks : 16th International Symposium on Neural Networks, Proceedings, Part II (pp. 180-189). Springer International Publishing.</i> <a href="https://doi.org/10.1007/978-3-030-22808-8_19"target="_blank"> DOI: 10.1007/978-3-030-22808-8_19</a> | |
dc.relation.haspart | <b>Artikkeli III:</b> Wang, X., Ristaniemi, T., & Cong, F. (2019). Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition. In <i>ICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 8588-8592). IEEE.</i> <a href="https://doi.org/10.1109/ICASSP.2019.8682737"target="_blank"> DOI: 10.1109/ICASSP.2019.8682737</a> | |
dc.relation.haspart | <b>Artikkeli IV:</b> Wang, Xiulin; Liu, Wenya; Toiviainen, Petri; Ristaniemi, Tapani; Cong, Fengyu (2020). Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition. <i>Journal of Neuroscience Methods, 330, 108502.</i> <a href="https://doi.org/10.1016/j.jneumeth.2019.108502"target="_blank"> DOI: 10.1016/j.jneumeth.2019.108502</a> | |
dc.relation.haspart | <b>Artikkeli V:</b> Xiulin Wang, Tapani Ristaniemi and Fengyu Cong. (2020). Fast Learnings of Coupled Nonnegative Tensor Decomposition Using Optimal Gradient and Low-rank Approximation. <i>Submitted.</i> | |
dc.rights | In Copyright | |
dc.subject | aivotutkimus | |
dc.subject | kuvantaminen | |
dc.subject | toiminnallinen magneettikuvaus | |
dc.subject | EEG | |
dc.subject | signaalianalyysi | |
dc.subject | signaalinkäsittely | |
dc.subject | matriisit | |
dc.subject | algoritmit | |
dc.subject | brain imaging data | |
dc.subject | coupled constraint | |
dc.subject | group analysis | |
dc.subject | nonnegative matrix/tensor factorization | |
dc.subject | sparse regularization | |
dc.title | Coupled Nonnegative Matrix/Tensor Factorization in Brain Imaging Data | |
dc.type | doctoral thesis | |
dc.identifier.urn | URN:ISBN:978-951-39-8407-6 | |
dc.contributor.tiedekunta | Faculty of Information Technology | en |
dc.contributor.tiedekunta | Informaatioteknologian tiedekunta | fi |
dc.contributor.yliopisto | University of Jyväskylä | en |
dc.contributor.yliopisto | Jyväskylän yliopisto | fi |
dc.type.coar | http://purl.org/coar/resource_type/c_db06 | |
dc.relation.issn | 2489-9003 | |
dc.rights.copyright | © The Author & University of Jyväskylä | |
dc.rights.accesslevel | openAccess | |
dc.type.publication | doctoralThesis | |
dc.format.content | fulltext | |
dc.rights.url | https://rightsstatements.org/page/InC/1.0/ | |