Local-to-global semi-supervised feature selection

Authors:
Mohammed Hindawi;Khalid Benabdeslem
Affiliations:
University of PARIS13 / LIPN, Villetaneuse, France;University of Lyon1 / LIRIS, Villeurbanne, France
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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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.