Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
SIAM Journal on Matrix Analysis and Applications
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dimension Reduction in Text Classification with Support Vector Machines
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Linear discriminant analysis (LDA) has been widely used for dimension reduction of data sets with multiple classes. The LDA has been recently extended to various generalized LDA methods that are applicable regardless of the relative sizes between the data dimension and the number of data items. In this paper, we propose several multiclass classifiers based on generalized LDA (GLDA) algorithms, taking advantage of the dimension reducing transformation matrix without requiring additional training or parameter optimization. A marginal linear discriminant classifier (MLDC), a Bayesian linear discriminant classifier (BLDC), and a one-dimensional BLDC are introduced for multiclass classification. Our experimental results illustrate that these classifiers produce higher ten-fold cross validation accuracy than kNN and centroid-based classifiers in the reduced dimensional space obtained from GLDA.