Dimensionality Reduction of Protein Mass Spectrometry Data Using Random Projection

  • Chen Change Loy
  • Weng Kin Lai
  • Chee Peng Lim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


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.


Principal Component Analysis Support Vector Machine Partial Little Square Dimensionality Reduction Projection Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chen Change Loy
    • 1
  • Weng Kin Lai
    • 1
  • Chee Peng Lim
    • 2
  1. 1.Grid Computing and Bioinformatics Lab, MIMOS BerhadKuala LumpurMalaysia
  2. 2.School of Electrical & Electronic EngineeringUniversity of Science MalaysiaNibong Tebal, PenangMalaysia

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