Handling missing values in kernel methods with application to microbiology data
We discuss several approaches that make possible for kernel methods to deal with missing
values for binary variables. The first two are extended kernels able to handle missing values …
values for binary variables. The first two are extended kernels able to handle missing values …
Making kernel density estimation robust towards missing values in highly incomplete multivariate data without imputation
R Leibrandt, S Günnemann - Proceedings of the 2018 SIAM International …, 2018 - SIAM
Density estimation is one of the most frequently used data analytics techniques. A major
challenge of real-world datasets is missing values, originating eg from sampling errors or …
challenge of real-world datasets is missing values, originating eg from sampling errors or …