This work studies the theoretical rules of feature selection in linear discriminant analysis (LDA) and a new feature selection method is proposed for sparse linear discriminant analysis. A l1 minimization method is used to select the important features from which LDA will be constructed. The asymptotic results of this proposed Two-stage LDA (TLDA) are studied, demonstrating that TLDA is an opti...

EMPs Extended morphological profiles EMPs Extended morphological profiles LDA Linear discriminant analysis LogDA Logarithmic discriminant analysis MLR Multinomial logistic regression MLRsubMRF Subspace-based multinomial logistic regression followed by Markov random fields MPs Morphological profiles MRFs Markov random fields PCA Principal component analysis QDA Quadratic discriminant analysis RH...

Linear Discriminant Analysis (LDA) often suffers from the small sample size problem when dealing with high dimensional face data. Random subspace can effectively solve this problem by random sampling on face features. However, it remains a problem how to construct an optimal random subspace for discriminant analysis and perform the most efficient discriminant analysis on the constructed random ...

Classification of Indian stock market data has always been a certain appeal for researchers. In this paper, first time combination of three supervised machine learning algorithms, classification and regression tree (CART) , linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are proposed for classification of Indian stock market data, which gives simple interpretation o...

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method coined local similarity based linear discriminan...

Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more ...

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new fea...

In this paper, we have integrated in a GMM based speaker identi cation system two di erent techniques: a) Maximum Likelihood Linear Regression (MLLR) transformation which adapts the system to the new environment based on modifying the continuous densities of the GMM mixtures. We apply the MLLR to perform environmental compensation by reducing a mismatch due to channel or additive noise e ects, ...