Sparse Group Bases for Multisubject fMRI Data

Sparse Group Bases for Multisubject fMRI Data

Considering that functional magnetic resonance imaging (fMRI) signals from multiple subjects (MS) can be represented together as a sum of common and a sum of distinct rank-1 matrices, a new MS dictionary learning (DL) algorithm named sparse group (common + distinct) bases (sgBACES) is pro...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Muhammad Usman Khalid
Μορφή: Άρθρο
Γλώσσα:English
Έκδοση: IEEE 2022-01-01
Σειρά:IEEE Access
Θέματα:
Sparse representation
dictionary learning
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>₀ constraint
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>₁ penalization
multi-subject analysis
fMRI
Διαθέσιμο Online:https://ieeexplore.ieee.org/document/9843989/

Διαδίκτυο

https://ieeexplore.ieee.org/document/9843989/

Παρόμοια τεκμήρια