Dimensionality reduction of protein mass spectrometry data using random projection

Loy, Chen Change, Lai, Weng Kin and Lim, Chee Peng 2006, Dimensionality reduction of protein mass spectrometry data using random projection. In King, Irwin, Wang, Jun, Chan, Laiwan and Wang, DeLiang (ed), Neural information processing, Springer, Berlin, Germany, pp.776-785, doi: 10.1007/11893257_86.

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Title Dimensionality reduction of protein mass spectrometry data using random projection
Author(s) Loy, Chen Change
Lai, Weng Kin
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Title of book Neural information processing
Editor(s) King, Irwin
Wang, Jun
Chan, Laiwan
Wang, DeLiang
Publication date 2006
Series Lecture notes in computer science; v. 4233
Chapter number 86
Total chapters 129
Start page 776
End page 785
Total pages 10
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) computational complexity
data processing
data reduction
mass spectrometry
principal component analysis
Summary Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.
Notes Proceedings Part II, 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006
ISBN 3540464816
ISSN 0302-9743
Language eng
DOI 10.1007/11893257_86
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1.1 Book chapter
Copyright notice ©2006, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048766

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