Please use this identifier to cite or link to this item:
Title: A study of dimensionality reduction as subspace data embedding linked by multidimensional scaling
Authors: Wang, Wan Qiu
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2005
Source: Wang, W. Q. (2005). A study of dimensionality reduction as subspace data embedding linked by multidimensional scaling. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: Some state-of-the-art dimensionality reduction techniques are reviewed and investigated in this thesis. Dimensionality reduction techniques can be categorized inti two serving different purposes. The first category is to mitigate the computational load and to address the Curse-of-Dimensionality, and the second category is to model the data spread or manifold. ISOMAP and LLE techniques are developed for the second purpose, and both of them are embedding techniques. By means of embedding, some data points in a higher dimensional space can be mapped into a lower dimensional space, provided that the pairwise distances are kept unchanged or within a small tolerant range. Some conven- tional category one techniques, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), are developed from variance analysis, but they also can be interpreted as embedding techniques through links to the metric Multidimensional Scaling (MDS).
DOI: 10.32657/10356/42687
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
WangWanQiu05.pdf2.71 MBAdobe PDFThumbnail

Page view(s) 50

Updated on Aug 2, 2021

Download(s) 20

Updated on Aug 2, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.