Unsupervised variable selection for kernel methods in systems biology

Jérôme J. Mariette (1) , Celine Brouard (1) , Remi Flamary (2) , Nathalie Vialaneix (1)

1 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
2 UNSA - Université de Nice Sophia-Antipolis

Kernel methods have proven to be useful and successful to analyse large-scale multi-omics datasets [Schölkopf et al., 2004]. However, as stated in [Hofmann et al., 2015, Mariette et al., 2017], these methods usually suffer from a lack of interpretability as the information of thousands descriptors is summarized in a few similarity measures, that can be strongly in uenced by a large number of irrelevant descriptors. To address this issue, feature selection is a widely used strategy: it consist in selecting the most promising features during or prior the analysis. However, most existing methods are proposed in a supervised framework [Tibshirani, 1996, Robnik-Sikonja and Kononenko, 2003, Lin and Tang, 2006]. In the unsupervised framework, the number of proposals is much less important, because there is no objective criterion or value on which to tune the quality of a given feature. Proposals thus aim at preserving at best the similarities between individuals like the SPEC approach [Zhao and Liu, 2007] or at recovering a latent cluster structure, like MCFS [Cai et al., 2010], NDFS [Li et al., 2012] and UDFS [Yang et al., 2011]. In this communication, we will present a feature selection algorithm that explicitly takes advantage of the kernel structure in an unsupervised fashion.

Domaines: Mathématiques [math]; Informatique [cs]

Jérôme J. Mariette, Celine Brouard, Remi Flamary, Nathalie Vialaneix. Unsupervised variable selection for kernel methods in systems biology. Journée Régionale de Bioinformatique et Biostatistique, Génopole Toulouse, Oct 2019, Toulouse, France. ⟨hal-02787253⟩