Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/35274
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Type: Conference paper
Title: Feature extraction from terahertz pulses for classification of RNA data via support vector machines
Author: Yin, X.
Ng, B.
Fischer, B.
Ferguson, B.
Mickan, S.
Abbott, D.
Citation: Micro- and Nanotechnology: Materials, Processes, Packaging, and Systems III, Jung-Chih Chiao, Andrew S. Dzurak, Chennupati Jagadish, David V. Thiel (eds.), pp. 641516 1-10
Publisher: SPIE
Publisher Place: USA
Issue Date: 2006
Series/Report no.: Proceedings of SPIE--the International Society for Optical Engineering ; 6415.
ISBN: 0819465232
9780819465238
ISSN: 0277-786X
Conference Name: Smart Materials, Nano- & Micro-Smart Systems (2006 : Adelaide, Australia)
Statement of
Responsibility: 
Xiaoxia Yin, Brian W.-H. Ng, Bernd Fischer, Bradley Ferguson, Samuel P. Mickan, and Derek Abbott
Abstract: This study investigates binary and multiple classes of classification via support vector machines (SVMs). A couple of groups of two dimensional features are extracted via frequency orientation components, which result in the effective classification of Terahertz (T-ray) pulses for discrimination of RNA data and various powder samples. For each classification task, a pair of extracted feature vectors from the terahertz signals corresponding to each class is viewed as two coordinates and plotted in the same coordinate system. The current classification method extracts specific features from the Fourier spectrum, without applying an extra feature extractor. This method shows that SVMs can employ conventional feature extraction methods for a T-ray classification task. Moreover, we discuss the challenges faced by this method. A pairwise classification method is applied for the multi-class classification of powder samples. Plots of learning vectors assist in understanding the classification task, which exhibit improved clustering, clear learning margins, and least support vectors. This paper highlights the ability to use a small number of features (2D features) for classification via analyzing the frequency spectrum, which greatly reduces the computation complexity in achieving the preferred classification performance.
Description: © 2006 COPYRIGHT SPIE--The International Society for Optical Engineering
RMID: 0020062990
DOI: 10.1117/12.695629
Appears in Collections:Electrical and Electronic Engineering publications

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