Spectral clustering under degree heterogeneity: a case for the random walk Laplacian

Autor: Modell, Alexander, Rubin-Delanchy, Patrick

This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree. Under a generalised random dot product graph, the embedding provides uniformly consistent e

Externí odkaz: http://arxiv.org/abs/2105.00987

Correcting for selection bias via cross-validation in the classification of microarray data

Autor: Geoffrey J. McLachlan, J. Zhu, Jonathan Chevelu

Publikováno v: N. Balakrishnan, Edsel A. Peña and Mervyn J. Silvapulle, eds., Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008), 364-376

There is increasing interest in the use of diagnostic rules based on microarray data. These rules are formed by considering the expression levels of thousands of genes in tissue samples taken on patients of known classification with respect to a numb

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c99ee3b9ee3c3c3cad463acde7d67885