TY - JOUR
T1 - Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios
AU - Mishra, Puneet
AU - Metz, Maxime
AU - Marini, Federico
AU - Biancolillo, Alessandra
AU - Rutledge, Douglas N.
N1 - Funding Information:
Authors are thankful to Prof. Jean Michel Roger for his constructive feedback during the development of the work.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/5/15
Y1 - 2022/5/15
N2 - Multi-block datasets are widely met in the chemometrics domain, and several data fusion approaches have recently been proposed to treat them. Apart from exploratory and predictive modelling, a key task in this context is feature selection which involves finding key complementary variables across multiple data blocks that jointly provide a good explanation of the response variables, revealing the key variables of the system. In that direction, a new method called response-oriented covariate selection (ROCS) is proposed here. ROCS is a direct extension of the covariance selection (CovSel) approach to multi-block scenarios, where the choice is based on a competition between variables in different blocks, as is done in the response-oriented sequential alternation (ROSA) method. The uniqueness of the ROCS method is its simplicity, fast execution speed, insensitivity to block order and scale-invariance. The evaluation of ROCS is presented using several multi-block modelling cases and by comparison with other variable selection methods.
AB - Multi-block datasets are widely met in the chemometrics domain, and several data fusion approaches have recently been proposed to treat them. Apart from exploratory and predictive modelling, a key task in this context is feature selection which involves finding key complementary variables across multiple data blocks that jointly provide a good explanation of the response variables, revealing the key variables of the system. In that direction, a new method called response-oriented covariate selection (ROCS) is proposed here. ROCS is a direct extension of the covariance selection (CovSel) approach to multi-block scenarios, where the choice is based on a competition between variables in different blocks, as is done in the response-oriented sequential alternation (ROSA) method. The uniqueness of the ROCS method is its simplicity, fast execution speed, insensitivity to block order and scale-invariance. The evaluation of ROCS is presented using several multi-block modelling cases and by comparison with other variable selection methods.
KW - Covariance selection (CovSel)
KW - Data fusion
KW - Multi-block data analysis
KW - Response-oriented sequential alternation (ROSA)
KW - Variable selection
UR - http://www.scopus.com/inward/record.url?scp=85127921964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127921964&partnerID=8YFLogxK
U2 - 10.1016/j.chemolab.2022.104551
DO - 10.1016/j.chemolab.2022.104551
M3 - Article
AN - SCOPUS:85127921964
VL - 224
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
M1 - 104551
ER -