Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Low dimension hierarchical subspace modelling of high dimensional data

Samko, Oksana 2009. Low dimension hierarchical subspace modelling of high dimensional data. PhD Thesis, Cardiff University.

[thumbnail of U585272.pdf] PDF - Accepted Post-Print Version
Download (14MB)

Abstract

Building models of high-dimensional data in a low dimensional space has become extremely popular in recent years. Motion tracking, facial animation, stock market tracking, digital libraries and many other different models have been built and tuned to specific application domains. However, when the underlying structure of the original data is unknown, the modelling of such data is still an open question. The problem is of interest as capturing and storing large amounts of high dimensional data has become trivial, yet the capability to process, interpret, and use this data is limited. In this thesis, we introduce novel algorithms for modelling high dimensional data with an unknown structure, which allows us to represent the data with good accuracy and in a compact manner. This work presents a novel fully automated dynamic hierarchical algorithm, together with a novel automatic data partitioning method to work alongside existing specific models (talking head, human motion). Our algorithm is applicable to hierarchical data visualisation and classification, meaningful pattern extraction and recognition, and new data sequence generation. Also during our work we investigated problems related to low dimensional data representation: automatic optimal input parameter estimation, and robustness against noise and outliers. We show the potential of our modelling with many data domains: talking head, motion, audio, etc. and we believe that it has good potential in adapting to other domains.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISBN: 9781303215087
Date of First Compliant Deposit: 30 March 2016
Last Modified: 19 Mar 2016 23:31
URI: https://orca.cardiff.ac.uk/id/eprint/54883

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics