Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data

Héctor Climente-González, Chloé-Agathe Azencott, Samuel Kaski, Makoto Yamada

Abstract: Finding non-linear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have important drawbacks, including among others lack of parsimony, non-convexity and computational overhead. Here we propose block HSIC Lasso, a non-linear feature selector that does not present the previous drawbacks.