Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37989
Title: Cross-weighted Fisher discriminant analysis for visualization of DNA microarray data
Authors: Zhang, X
Myers, CL
Kung, SY
Keywords: DNA
Gaussian distribution
Data mining
Data visualisation
Learning (artificial intelligence)
Medical signal processing
Signal classification
Issue Date: 2004
Source: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing : proceedings : May 17-21, 2004, Fairmont Queen Elizabeth Hotel, Montreal, Quebec, Canada, p. V-589-592 How to cite?
Abstract: Fisher discriminant analysis (DA) has recently shown promise in dimensionality reduction of high dimensional DNA data. However, the 1D projection provided by this method is an optimal Bayesian classifier only when the intraclass data patterns are purely Gaussian distributed. Unfortunately, it has been well recognized that most DNA expression data are much more realistically represented by a Gaussian mixture model (GMM), which allows for multiple cluster centroids per class. When a data set from such a GMM is projected onto a 1D subspace, its inherent multi-modal nature may be partially or completely obscured. Consequently, traditional Fisher DA is quite inadequate when higher dimensional visualization (e.g. 2D or 3D) is necessary. The proposed technique addresses this problem and makes use of combined supervised and unsupervised learning techniques for several DNA microarray signal processing functions, including intraclass cluster discovery, optimal projection, and identification/selection of responsible gene groups. In particular, a cross-weighted Fisher DA is proposed and its abilities to reduce dimensionality and to visualize data sets are evaluated.
URI: http://hdl.handle.net/10397/37989
ISBN: 0-7803-8484-9
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2004.1327179
Appears in Collections:Conference Paper

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