Rbm-od: a restricted Boltzmann machine framework for outlier detection

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dc.contributor.authorHoeksema, Brady F.
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorZhang, John Z.
dc.date.accessioned2025-10-16T20:05:24Z
dc.date.available2025-10-16T20:05:24Z
dc.date.issued2025
dc.degree.levelMasters
dc.description.abstractThis thesis explores the use of Restricted Boltzmann Machines (RBMs), a class of unsupervised generative neural networks, for detecting outliers through data generation and representation-based comparison. Outlier detection (OD) is a critical task in domains where rare or anomalous patterns may indicate errors, fraud, or unexpected behaviour in data. The primary contribution of this work is a unified framework for RBM-based outlier detection that emphasizes data generation as a detection strategy. We explore multiple model variants, including single RBMs, ensembles of RBMs, stacked RBMs, and ensembles of stacked RBMs, each offering distinct advantages in representing complex data patterns. By generating synthetic samples from trained RBMs and comparing them to input data, the approach enables unsupervised detection of unusual or unexpected instances. This gener ative perspective distinguishes RBM-OD from traditional methods and provides a flexible foundation for future extensions.
dc.embargoNo
dc.identifier.urihttps://hdl.handle.net/10133/7171
dc.language.isoen
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
dc.publisher.departmentDepartment of Mathematics and Computer Science
dc.publisher.facultyArts and Science
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)
dc.subjectoutlier detection
dc.subjectRBM
dc.subjectrestricted Botlzmann machine
dc.subjectmachine learning
dc.subjectrbm
dc.subject.lcshDissertations, Academic
dc.subject.lcshArtificial intelligence
dc.subject.lcshData mining
dc.subject.lcshBig data
dc.subject.lcshComputational intelligence
dc.subject.lcshOutliers (Statistics)
dc.subject.lcshNeural networks (Computer science)
dc.titleRbm-od: a restricted Boltzmann machine framework for outlier detection
dc.typeThesis
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