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    Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis

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    GT-CSE-06-18.pdf (218.0Kb)
    Date
    2006
    Author
    Ye, Jieping
    Janardan, Ravi
    Li, Qi
    Park, Haesun
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    Abstract
    High-dimensional data appear in many applications of data mining, machine learning, and bioinformatics. Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature reduction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, an algorithm called ULDA/QR is proposed to simplify the previous implementation of ULDA. Then the ULDA/GSVD algorithm is proposed based on a novel optimization criterion, to address the singularity problem which occurs in undersampled problems, where the data dimension is larger than the data size. The criterion used is the regularized version of the one in ULDA/QR. Surprisingly, our theoretical result shows that the solution to ULDA/GSVD is independent of the value of the regularization parameter. Experimental results on various types of datasets are reported to show the effectiveness of the proposed algorithm and to compare it with other commonly used feature reduction algorithms.
    URI
    http://hdl.handle.net/1853/14447
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    • College of Computing Technical Reports [506]
    • School of Computational Science and Engineering Technical Reports [37]

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