Show simple item record

dc.contributor.authorSenanayake, Damith Asanka
dc.date.accessioned2020-05-22T02:15:21Z
dc.date.available2020-05-22T02:15:21Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11343/239181
dc.description© 2020 Damith Asanka Senanayake
dc.description.abstractRecent advances in experimental technologies have facilitated the gathering of data with thousands of variables. Because of this, modern data analysis tasks often encounter high dimensional data, which are challenging to analyse. Such analysis is made more difficult with the lack of ground-truth. In this thesis, I have explored two aspects of high-dimensional exploratory data analysis: 1) Dimensionality Reduction and Visualization of high-dimensional data to gain insights into the structure of the data and, 2) Extraction and interpretation of feature subsets (motifs) which explain the structure of high-dimensional data. I have presented methods that are built on concepts of Neural Networks - a powerful modern branch of optimization techniques. Through my work, I have shown that using relatively infrequently used variants of neural networks and building on their concepts (e.g. vector quantization and Hebbian Learning) we can produce powerful dimensionality reduction methods that address major gap areas of research. I have further shown that using unsupervised deep neural networks with contextual regularization, we can produce a framework to extract minimal motifs that optimally reconstruct complex nonlinear structures present in high dimensional data.
dc.rightsTerms and Conditions: Copyright in works deposited in Minerva Access is retained by the copyright owner. The work may not be altered without permission from the copyright owner. Readers may only download, print and save electronic copies of whole works for their own personal non-commercial use. Any use that exceeds these limits requires permission from the copyright owner. Attribution is essential when quoting or paraphrasing from these works.
dc.subjectData Mining
dc.subjectMachine Learning
dc.subjectHigh-dimensional Data
dc.subjectMultivariate Data
dc.subjectNeural Networks
dc.subjectSelf Organization
dc.subjectDeep Learning
dc.subjectSingle Cell Genomics
dc.subjectRNA-Seq
dc.subjectFlow Cytometry
dc.titleExploratory Analysis of Highly Dimensional Data: Parametric Methods for Dimensionality Reduction, Visualization and Feature Extraction with Applications in Computational Biology
dc.typePhD thesis
melbourne.affiliation.departmentMechanical Engineering
melbourne.affiliation.facultyEngineering
melbourne.thesis.supervisornameSaman Halgamuge
melbourne.contributor.authorSenanayake, Damith Asanka
melbourne.tes.fieldofresearch1080108 Neural, Evolutionary and Fuzzy Computation
melbourne.tes.fieldofresearch2060102 Bioinformatics
melbourne.tes.fieldofresearch3080109 Pattern Recognition and Data Mining
melbourne.tes.confirmedtrue
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record