TY  - CONF
AU  - Raimondo, Federico
AU  - Antonopoulos, Georgios
AU  - Eickhoff, Simon
AU  - Patil, Kaustubh
TI  - FASTGPR: DIVIDE-AND-CONQUER TECHNIQUE IN NEUROIMAGING DATA SHORTENS TRAINING TIME AND IMPROVES ACCURACY
M1  - FZJ-2024-07621
PY  - 2024
AB  - Gaussian process regression (GPR) has shown great potential for studying healthy aging and disease via brain-age prediction (BAP) using structural MRI[1].A big drawback of GPR is the training complexity which is an O(N^3) operation (N=number of data points).The need for expansive datasets and the high dimensionality of MRI data, renders the training of GPR impractical with conventional computing resources.We investigated whether a divide-and-conquer approach can be used together with the GPR model
T2  - International Symposium of Biomedical Imaging 2024
CY  - 27 May 2024 - 30 May 2024, Athens (Greece)
Y2  - 27 May 2024 - 30 May 2024
M2  - Athens, Greece
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2024-07621
UR  - https://juser.fz-juelich.de/record/1034875
ER  -