首页|Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios
Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
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.
Multi-block data analysisData fusionVariable selectionCovariance selection (CovSel)Response-oriented sequential alternation(ROSA)PLS
Mishra, Puneet、Metz, Maxime、Marini, Federico、Biancolillo, Alessandra、Rutledge, Douglas N.
NSTL
Elsevier
