Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality.

ABSTRACT: The application of machine learning to inference problems in biology is dominated by supervised learning problems of regression and classification, and unsupervised learning problems of clustering and variants of low-dimensional projections for visualization. A class of problems that have not gained much attention is detecting outliers in datasets, arising from reasons such as gross experimental, reporting or labelling errors. These could also be small parts of a dataset that are functionally distinct from the majority of a population. Outlier data are often identified by considering the probability density of normal data and comparing data likelihoods against some threshold. This classical approach suffers from the curse of dimensionality, which is a serious problem with omics data which

SUBMITTER: Shetta O 

PROVIDER: S-EPMC7062061 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature