Spectral Simplicial Theory for Feature Selection and Applications to Genomics
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The scale and complexity of modern data sets and the limitations associated with testing large numbers of hypotheses underline the need for feature selection methods. Spectral techniques rank features according to their degree of consistency with an underlying metric structure, but their current graph-based formulation restricts their applicability to point features. We extend spectral methods for feature selection to abstract simplicial complexes and present a general framework which can be applied to 2-point and higher-order features. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score in the case of point features. We demonstrate the utility of spectral simplicial methods for feature selection with several examples of application to the analysis of gene expression and multi-modal genomic data. Our results provide a unifying perspective on topological data analysis and manifold learning approaches.
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