The aim of this project is to classify the mammographic masses as benign or malignant using texture and shape features. A set of 73 mammograms is used for the analysis, out of which 41 are benign and 32 are malignant. Manually segmented masses are obtained from the DDSM, USF database [2]. Texture and shape features are extracted from the manually segmented masses. Stepwise linear discriminant a...

In the last decade, the renaissance of interest in discriminant analysis has been primarily motivated by possible applications to tumor classification using highdimensional microarray-based data. In this thesis, we do three things: 1. First, we introduce a new regularizing covariance estimation procedure we refer to as SHIP: SHrinking and Incorporating Prior knowledge. The resulting covariance ...

In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improvements in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the E...

A significant amount of speech is typically required for speaker verification system development and evaluation, especially in the presence of large intersession variability. This paper introduces a source and utterance-duration normalized linear discriminant analysis (SUN-LDA) approaches to compensate session variability in short-utterance i-vector speaker verification systems. Two variations ...

The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted...

In this paper, the performance of different generative methods for the classification of cervical nuclei are compared in order to detect cancer of cervix. These methods include classical Bayesian approaches, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) or Mixture Discriminant Analysis (MDA) and a high-dimensional approach (HDDA) recently developed. The class...

1 School of Mathematics and Computation Science Sun Yat-sen University Guangzhou, P. R. China, [email protected] 2 Department of Electronics & Communication Engineering, School of Information Science & Technology Sun Yat-sen University Guangzhou, P. R. China, [email protected] 3 Guangdong Province Key Laboratory of Information Security, P. R. China 4 Center for Biometrics and Security Rese...

The support vector machine (SVM), as a novel type of learning machine, was used to develop a classification model of carcinogenic properties of 148 N-nitroso compounds. The seven descriptors calculated solely from the molecular structures of compounds selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The obtained results confirmed the discrimi...

It has been demonstrated that the Linear Discriminant Analysis (LDA) approach outperforms the Principal Component Analysis (PCA) approach in face recognition tasks. Due to the high dimensionality of a image space, many LDA based approaches, however, first use the PCA to project an image into a lower dimensional space or so-called face space, and then perform the LDA to maximize the discriminato...

Techniques that can introduce low dimensional feature representation with enhanced discriminatory power are important in face recognition systems. This paper presents one of the symbolic factor analysis method i.e., symbolic Linear Discriminant Analysis (symbolic LDA) method for face representation and recognition. Classical factor analysis methods extract features, which are single valued in n...