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.

Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios / Mishra, P.; Metz, M.; Marini, F.; Biancolillo, A.; Rutledge, D. N.. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 224:(2022), pp. 1-9. [10.1016/j.chemolab.2022.104551]

Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios

Marini F.;
2022

Abstract

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.
2022
covariance selection (CovSel); data fusion; multi-block data analysis; response-oriented sequential alternation (ROSA); variable selection
01 Pubblicazione su rivista::01a Articolo in rivista
Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios / Mishra, P.; Metz, M.; Marini, F.; Biancolillo, A.; Rutledge, D. N.. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 224:(2022), pp. 1-9. [10.1016/j.chemolab.2022.104551]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1640125
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