Partially supervised feature selection with regularized linear models

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Learning Credible Models (2017, methodology): “…w ∗ | θ | [ i ] is the i th largest parameter sorted by magnitude; and ⊙ is the training time, Sun et al . explore using features identified as relevant Vapnik et al . explore the theory of learning with privileged information In the linear setting, the method simplifies to l 2 regularization on”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “Benabdeslem and Hindawi [146], [147], Helleputte and Dupont [148], and Kalakech et al.”
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods (2010, background): “Recent results show that incorporation of prior knownledge in the biomarker selection process (Helleputte and Dupont, 2009b) or model estimation across several related datasets (Helleputte and Dupont, 2009a) are worth investigating along these lines.”
Feature Selection as a One-Player Game (2010, background): “Quite a few other regularization approaches, mostly considering linear hypothesis spaces, have been considered along the same lines (Guyon et al., 2002; Chan et al., 2007; Zhang, 2008b; Helleputte & Dupont, 2009).”