Discriminant analysis, including linear discriminant analysis (LDA) and quadratic (QDA), is a popular approach to classification problems. It well known that LDA suboptimal analyze heteroscedastic data, for which QDA would be an ideal tool. However, less helpful when the number of features in data set moderate or high, its variants often perform better due their robustness against dimensionalit...

In speech recognition, a speech/non-speech detection must be robust to noise. In this work, a new method for speech/nonspeech detection using a Linear Discriminant Analysis (LDA) applied to Mel Frequency Cepstrum Coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech/non-speech detection system detects...

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...

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy. This paper introduces a new wrapper approach to this difficult task where a Genetic Algorithm (GA) is combined with Fisher’s Linear Discriminant Analysis (LDA). This LDA-based GA algorithm has the major characteristic that the GA uses not only a LDA classif...

Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for bina...

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are well known techniques for face recognition. Both PCA and LDA by themselves have good recognition rates. We propose Canonical Correlation Analysis (CCA) for combining two feature extractors to improve the performance of the system, by obtaining the advantages of both. CCA finds the transformation for each extractor dat...

Both two dimensional principal component analysis and fisher linear discriminant analysis are successful face recognition algorithms. Recognition rate, time complexity can be improved by combining the two algorithms with the very powerful tool discrete wavelet transform. Experiments on the ORL face database show that the proposed method outperforms PCA, LDA, DWT+LDA algorithms in terms of recog...

In face recognition literature, major approaches based on holistic templates and geometrical local features have been taken. Both approaches have certain advantages and disadvantages. In this paper, we explore a new method which integrates the above two approaches. Among many speciic systems, we select LDA (Linear Discriminant Analysis) and MPF (Matching Pursuit Filter) as the representative fr...

Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+dynamic features in most speech recognition tasks. It is reasonable to regularize LDA transform computation for stability. In this paper, we regularize LDA and heteroschedastic LDA transforms usin...

To precisely model the time dependency of features is one of the important issues for speech recognition. Segmental unit input HMM with a dimensionality reduction method has been widely used to address this issue. Linear discriminant analysis (LDA) and heteroscedastic extensions, e.g., heteroscedastic linear discriminant analysis (HLDA) or heteroscedastic discriminant analysis (HDA), are popula...