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 reco...

OBJECTIVE In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. METHODS In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have...

MOTIVATION DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The perform...

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature wh...

Extending popular histogram representations of local motion patterns, we present a novel weighted integration method based on an assumption that a motion importance should be changed by its appearance to obtain better recognition accuracies. The proposed integration method of motion and appearance patterns can weight information involving “what is moving” by discriminant way. The discriminant w...

Objective in this paper, we have done Mahalanobis Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. Methods In accordance with the strength of  wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we ...

Features that model temporal aspects of phonemes are important in speech recognition. One method is to use linear discriminant analysis (LDA) to find discriminative features from a spectrotemporal input formed by concatenating consecutive frames of short-time spectrum features. Others use e.g. neural networks to process longer span spectral segments to improve recognition accuracy. Still the mo...

Automatic facial expression recognition is an interesting and challenging subject in signal processing, pattern recognition, artificial intelligence, etc. In this paper, a new method of facial expression recognition based on local binary patterns (LBP) and local Fisher discriminant analysis (LFDA) is presented. The LBP features are firstly extracted from the original facial expression images. T...

We propose a new approach to classifying diagnostic crystallographic images. The classification procedure uses a three-nearest neighbor algorithm based on the Euclidean distance. The image segmentation is conducted in a spatial domain, with energy values in each segment serving as features. Based on the value of the separability criterion used in discriminant analysis, most informative features...

Objective in this paper, we have done Fisher discriminant analysis to Electroencephalogram (EEG) data of experiment objects which are recorded impersonally, come up with a relatively accurate method used in feature extraction and classification decisions. The present study is the groundwork analysis for other analysis in EEG study. Methods In accordance with the strength of  wave, the head ele...