Local-To-Global Semi-Supervised Feature selection
Résumé
Variable-weighting approaches are well-known in the context of embedded feature selection. Generally, feature selection is performed in a global way, when the algorithm selects a single cluster-independent subset of features (global feature selection). However, other approaches 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 weighs 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.