Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality
Sign Up to like & getrecommendations! Published in 2020 at "Royal Society Open Science"
DOI: 10.1098/rsos.190714
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… read more here.
Keywords: genomic data; dimensionality; outlier detection; robust subspace ... See more keywords
Online Robust Subspace Clustering With Application to Power Grid Monitoring
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DOI: 10.1109/access.2023.3257357
Abstract: In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of… read more here.
Keywords: robust subspace; power grid; subspace clustering; online ... See more keywords
An Overview of Robust Subspace Recovery
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DOI: 10.1109/jproc.2018.2853141
Abstract: This paper will serve as an introduction to the body of work on robust subspace recovery. Robust subspace recovery involves finding an underlying low-dimensional subspace in a data set that is possibly corrupted with outliers.… read more here.
Keywords: robust subspace; overview robust; subspace; subspace recovery ... See more keywords