Thesis / Dissertation

Exploratory Analysis of Highly Dimensional Data: Parametric Methods for Dimensionality Reduction, Visualization and Feature Extraction with Applications in Computational Biology

Damith Asanka Senanayake, Saman Halgamuge (ed.)

Published : 2020

Abstract

Recent 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 ..

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University of Melbourne Researchers