bibtype	M - Monography Chapter
ARLID	0479581
utime	20240103214711.8
mtime	20171016235959.9
SCOPUS	85044017216
WOS	000449616400017
DOI	10.1007/978-3-319-55556-0_15
title (primary) (eng)	Multi-Penalty Regularization for Detecting Relevant Variables
specification	book_pages 948 page_count 27 s. media_type P
serial	ARLID cav_un_epca*0479580 ISBN 978-3-319-55555-3 title Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science page_num 889-916 publisher place Cham name Springer year 2017 editor name1 Le Gia name2 Q. T. editor name1 Mayeli name2 A. editor name1 Mhaskar name2 H. editor name1 Zhou name2 D.-X.
keyword	detecting relevant variables
keyword	multi-penalty regularization
keyword	behavior of discrepancies
author (primary)	ARLID cav_un_auth*0247122 name1 Hlaváčková-Schindler name2 Kateřina institution UTIA-B full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0333744 name1 Naumova name2 V. country NO
author	ARLID cav_un_auth*0353135 name1 Pereverzyev name2 S. Jr. country AT
source	url http://library.utia.cas.cz/separaty/2017/AS/hlaváčková-schindler-0479581.pdf
cas_special	abstract (eng) In this paper, we propose a new method for detecting relevant variables from a priori given high-dimensional data under the assumption that input-output relation is described by a nonlinear function depending on a few variables. The method is based on the inspection of the behavior of discrepancies of a multi-penalty regularization with a component-wise penalization for small and large values of regularization parameters. We provide a justification of the proposed method under a certain condition on sampling operators. The effectiveness of the method is demonstrated in an example with simulated data and in the reconstruction of a gene regulatory network. In the latter example, the obtained results provide clear evidence of the competitiveness of the proposed method with respect to the state-of-the-art approaches. RIV BD FORD0 10000 FORD1 10100 FORD2 10102 reportyear 2018 num_of_auth 3 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0276739 confidential S mrcbC83 RIV/67985556:_____/17:00479581!RIV18-AV0-67985556 191975711 Doplnění UT WOS a Scopus mrcbC86 3+4 Article Mathematics Applied mrcbC86 3+4 Article Mathematics Applied mrcbC86 3+4 Article Mathematics Applied arlyear 2017 mrcbU14 85044017216 SCOPUS mrcbU24 PUBMED mrcbU34 000449616400017 WOS mrcbU63 cav_un_epca*0479580 Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science 978-3-319-55555-3 889 916 Cham Springer 2017 Applied and Numerical Harmonic Analysis mrcbU67 Pesenson I. 340 mrcbU67 340 Le Gia Q. T. mrcbU67 340 Mayeli A. mrcbU67 340 Mhaskar H. mrcbU67 340 Zhou D.-X.