RT Journal Article
T1 A novel framework for parsimonious multivariate analysis
A1 Muñoz Romero, Sergio
A1 Gómez Verdejo, Vanessa
A1 Parrado Hernández, Emilio
AB This paper proposes a framework in which a multivariate analysis method (MVA) guides a selection of input variables that leads to a sparse feature extraction. This framework, called parsimonious MVA, is specially suited for high dimensional data such as gene arrays, digital pictures, etc. The feature selection relies on the analysis of consistency in the behaviour of the input variables through the elements of an ensemble of MVA projection matrices. The ensemble is constructed following a bootstrap that builds on an efficient and generalized MVA formulation that covers PCA, CCA and OPLS. Moreover, it allows the estimation of the relative relevance of each selected input variable. Experimental results point out that the features extracted by the parsimonious MVA have excellent discrimination power, comparing favorably with state-of-the-art methods, and are potentially useful to build interpretable features. Besides, the parsimonious feature extractor is shown to be robust against to parameter selection, as we all computationally efficient.
PB Elsevier
SN 0031-3203
YR 2017
FD 2017-11
LK https://hdl.handle.net/10016/33842
UL https://hdl.handle.net/10016/33842
LA eng
NO This work has been partly funded by the Spanish MINECO grant TEC2014-52289R and TEC2013-48439-C4-1-R. The authors want to thank the action editor and the reviewers for their valuable feedback.
DS e-Archivo
RD 27 jul. 2024