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A kernel maximum uncertainty discriminant analysis and its application to face recognition
(2009-02-05) Carlos E. Thomaz; GIRALDI, G. A.
In this paper, we extend the Maximum uncertainty Linear Discriminant Analysis (MLDA), proposed recently for limited sample size problems, to its kernel version. The new Kernel Maximum uncertainty Discriminant Analysis (KMDA) is a two-stage method composed of Kernel Principal Component Analysis (KPCA) followed by the standard MLDA. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other existing kernel discriminant methods, such as Generalized Discriminant Analysis (GDA) and Regularized Kernel Discriminant Analysis (RKDA). The classification results indicate that KMDA performs as well as GDA and RKDA, with the advantage of being a straightforward stabilization approach for the within-class scatter matrix that uses higher-order features for further classification improvements.
Linear discriminant analysis versus artificial neural network as classifiers for elbow angular position recognition purposes
(2012-02-01) Castro, M.C.F.
The increasing popularity of an Artificial Neural Network for pattern recognition and the absence of comparative studies showing its real superiority over Discriminant Analysis Methods motivated the present study, aiming at comparing the accuracy levels achieved for a Feed-Forward Multilayer Perceptron (MLP) and a Linear Discriminant Analysis (RLDA) applied to myoelectric signals to classify elbow angular positions. The results showed that there were no significant differences (t-student test p<0.05) between the average classification accuracies achieved for both methods even with the search of configuration parameters more appropriate to each situation. Both methods achieved average classification accuracies above 80% for a number of classes up to 4. However, 5 subjects achieved good results in a 5-class setup, which means a 20o shift between consecutive classes. Considering that for MLP there is an effort to define the architecture parameters and also learning parameters, its use is only justified if there is a need of generalization that cannot be achieved by the RLDA that does not require the predefinition of parameters, it is practical and fast, and performs very well.
EEG motor imagery classification of upper limb movements
(2013-02-11) Castro, M.C.F.; GALHIANNE, J. P. D. O. P.; COLOMBINI, E. L.
C EEG channel data are usually used when building systems that aim at distinguishing among right and left hand movements. Few alternatives use multichannel systems when bigger sets of motor imagery are subject to classification and more inputs are required. In this context, this work proposes the use of 8 EEG channels (F, C, P, and O), disposed in a non-conventional set up, to classify up to 4 motor imagery of the upper limbs through a Linear Discriminant Analysis classifier. A spatial feature selection, prior to classification, is applied in order to improve the classification accuracy. For the many channel combinations tested, results suggest that, in addition to the motor areas, other brain areas should be considered. For the proposed system, the best classification accuracy was achieved when distinguishing between left arm and left hand (89.74%) and using only the electrodes in F areas. For the right versus left hand a 71.80% rate was obtained, with electrodes either in P and O areas or in F and P areas. To discriminate between arms and hands, independently of the body side, the best score was 83.33%, for F and P channels, whereas for right and left limbs the best score was 66.02%, with only P channels. The best classification accuracy for the 4 movement problem achieved 50.00%, using all electrodes.

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