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, ...

The Fisher linear discriminant analysis (LDA) is a classical method for classification and dimension reduction jointly. A major limitation of the conventional LDA is a so-called singularity issue. Many LDA variants, especially two-stage methods such as PCA+LDA and LDA/QR, were proposed to solve this issue. In the two-stage methods, an intermediate stage for dimension reduction is developed befo...

Representing high-volume and high-order data is an essential problem, especially in machine learning field. Although existing two-dimensional (2D) discriminant analysis achieves promising performance, the single and linear projection features make it difficult to analyze more complex data. In this paper, we propose a novel convolutional two-dimensional linear discriminant analysis (2D LDA) meth...

This paper 1 proposes a method to extract nonlinear discriminant features from given input measurements by using outputs of multilayer Perceptron (MLP). Linear Discriminant Analysis (LDA) is one of the best known methods to construct linear features which are suitable for class discrimination. Otsu showed that LDA can be extended to nonlinear if we can estimate Bayesian a posteriori probabiliti...

Driving under the influence (DUI) is a synonymous term that represents the criminal offense of operating a motor vehicle while being under the influence of alcohol. Semiconductor oxides such as SnO2, TiO2, and ZnO have been more successfully employed as sensing materials compare to organic semiconductors for the detection of ethanol gas concentration. This work investigates the fabrication and ...

Sufficient dimension reduction methods provide effective ways to visualize discriminant analysis problems. For example, Cook and Yin (2001) showed that the dimension reduction method of sliced average variance estimation (save) identifies variates that are equivalent to a quadratic discriminant analysis (qda) solution. This article makes this connection explicit to motivate the use of save vari...

We propose a novel ensemble classification technique called the Nearest Instance Centroid Estimation (NICE) LDA algorithm. Our algorithm (inspired from NICE KLMS) performs a combination of two weak classifiers threshold based clustering and linear discriminant classification to achieve stateof-the-art results on various high dimensional UCI datasets. We discuss the important ways in which our m...

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...

We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional linear discriminant analysis (FLDA), is particularly useful when only fragments of the curves are observed. All the techniques associated with LDA can be extended for use with FLDA. In particular ...