Massively-parallel best subset selection for ordinary least-squares regression
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Massively-parallel best subset selection for ordinary least-squares regression. / Gieseke, Fabian; Polsterer, Kai Lars; Mahabal, Ashish; Igel, Christian; Heskes, Tom.
2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. IEEE, 2017. p. 1-8.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Massively-parallel best subset selection for ordinary least-squares regression
AU - Gieseke, Fabian
AU - Polsterer, Kai Lars
AU - Mahabal, Ashish
AU - Igel, Christian
AU - Heskes, Tom
PY - 2017
Y1 - 2017
N2 - Selecting an optimal subset of k out of d features for linear regression models given n training instances is often considered intractable for feature spaces with hundreds or thousands of dimensions. We propose an efficient massively-parallel implementation for selecting such optimal feature subsets in a brute-force fashion for small k. By exploiting the enormous compute power provided by modern parallel devices such as graphics processing units, it can deal with thousands of input dimensions even using standard commodity hardware only. We evaluate the practical runtime using artificial datasets and sketch the applicability of our framework in the context of astronomy.
AB - Selecting an optimal subset of k out of d features for linear regression models given n training instances is often considered intractable for feature spaces with hundreds or thousands of dimensions. We propose an efficient massively-parallel implementation for selecting such optimal feature subsets in a brute-force fashion for small k. By exploiting the enormous compute power provided by modern parallel devices such as graphics processing units, it can deal with thousands of input dimensions even using standard commodity hardware only. We evaluate the practical runtime using artificial datasets and sketch the applicability of our framework in the context of astronomy.
KW - graphics processing units
KW - least squares approximations
KW - optimisation
KW - parallel processing
KW - regression analysis
KW - sensitivity analysis
KW - input dimensions
KW - linear regression models
KW - massively-parallel best subset selection
KW - optimal feature subsets
KW - optimal subset
KW - ordinary least-squares regression
KW - subset selection
KW - Computational modeling
KW - Graphics processing units
KW - Instruction sets
KW - Optimization
KW - Runtime
KW - Task analysis
KW - Training
U2 - 10.1109/SSCI.2017.8285225
DO - 10.1109/SSCI.2017.8285225
M3 - Article in proceedings
SP - 1
EP - 8
BT - 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings
PB - IEEE
T2 - 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Y2 - 27 November 2017 through 1 December 2017
ER -
ID: 195159858