Local-to-global semi-supervised feature selection

Abstract

Variable-weighting approaches are well-known in the context of embedded feature selection. Generally, this task is performed in a global way, when the algorithm selects a single cluster-independent subset of features (global feature selection). However, there exist other approaches that aim to select cluster-specific subsets of features (local feature selection). Global and local feature selection have different objectives, nevertheless, in this paper we propose a novel embedded approach which locally weights the variables towards a global feature selection. The proposed approach is presented in the semi-supervised paradigm. Experiments on some known data sets are presented to validate our model and compare it with some representative methods.