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. Attached Files Name Description MIMEType Size Downloads Title Dimensionality reduction of protein mass spectrometry data using random projection Author(s) Loy, Chen Change Lai, Weng Kin 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 diagnosis mass spectrometry principal component analysis proteins 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 9783540464815 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 Document type: Book Chapter Collections: Institute for Frontier Materials GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 26 Sep 2012, 09:25:12 EST Tue, 27 Nov 2012, 10:02:49 EST Tue, 27 Nov 2012, 10:22:57 EST Tue, 27 Nov 2012, 10:31:01 EST Mon, 10 Dec 2012, 08:47:48 EST Tue, 24 Jun 2014, 09:49:49 EST Thu, 20 Nov 2014, 12:08:50 EST Sat, 21 Oct 2017, 02:05:16 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 710 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Wed, 26 Sep 2012, 09:25:12 EST

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