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Dimensionality reduction of protein mass spectrometry data using random projection
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.
History
Title of book
Neural information processingSeries
Lecture notes in computer science; v. 4233Chapter number
86Pagination
776 - 785Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743ISBN-13
9783540464815ISBN-10
3540464816Language
engNotes
Proceedings Part II, 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006Publication classification
B1.1 Book chapterCopyright notice
2006, SpringerExtent
129Editor/Contributor(s)
I King, J Wang, L Chan, D WangUsage metrics
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No categories selectedKeywords
computational complexitydata processingdata reductiondiagnosismass spectrometryprincipal component analysisproteinsScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsImaging Science & Photographic TechnologyComputer ScienceOVARIAN-CANCERSERUMCLASSIFICATION
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