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
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
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