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

Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality
Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality
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 are often found in very high dimensions. We develop an outlier detection method based on structured low-rank approximation methods. The objective function includes a regularizer based on neighbourhood information captured in the graph Laplacian. Results on publicly available genomic data show that our method robustly detects outliers whereas a density-based method fails even at moderate dimensions. Moreover, we show that our method has better clustering and visualization performance on the recovered low-dimensional projection when compared with popular dimensionality reduction techniques.
Dimensionality reduction, Genomics, High-dimensional data, Outlier detection
2054-5703
Shetta, Omar
168fd473-4857-42ce-8c4a-b4e83740462b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Shetta, Omar
168fd473-4857-42ce-8c4a-b4e83740462b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Shetta, Omar and Niranjan, Mahesan (2020) Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality. Royal Society Open Science, 7 (2), [190714]. (doi:10.1098/rsos.190714).

Record type: Article

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 are often found in very high dimensions. We develop an outlier detection method based on structured low-rank approximation methods. The objective function includes a regularizer based on neighbourhood information captured in the graph Laplacian. Results on publicly available genomic data show that our method robustly detects outliers whereas a density-based method fails even at moderate dimensions. Moreover, we show that our method has better clustering and visualization performance on the recovered low-dimensional projection when compared with popular dimensionality reduction techniques.

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rsos.190714 - Version of Record
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More information

Accepted/In Press date: 12 December 2019
e-pub ahead of print date: 5 February 2020
Published date: February 2020
Keywords: Dimensionality reduction, Genomics, High-dimensional data, Outlier detection

Identifiers

Local EPrints ID: 437876
URI: http://eprints.soton.ac.uk/id/eprint/437876
ISSN: 2054-5703
PURE UUID: 7d8f7193-309b-4847-8a59-90a9c7c4b4d3
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 21 Feb 2020 17:31
Last modified: 09 Jan 2022 03:26

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Contributors

Author: Omar Shetta
Author: Mahesan Niranjan ORCID iD

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