Results 1 
9 of
9
Distinguishing Word Senses in Untagged Text
 In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing
"... This paper describes an experimental com parison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. ..."
Abstract

Cited by 75 (17 self)
 Add to MetaCart
(Show Context)
This paper describes an experimental com parison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text.
A Maximum Uncertainty LDAbased approach for Limited Sample Size Problems  with . . .
 IN PROC. MICCAI’04, VOL. LNCS 3216
, 2004
"... A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the withinclass scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or preprocessed features availab ..."
Abstract

Cited by 16 (6 self)
 Add to MetaCart
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the withinclass scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or preprocessed features available, but the total number of training patterns is limited and commonly less than the dimension of the feature space. In this paper, a new LDAbased method is proposed. It is based on a straighforward stabilisation approach for the withinclass scatter matrix. In order to evaluate its effectiveness, experiments on face recognition using the wellknown ORL and FERET face databases were carried out and compared with other LDAbased methods. The results indicate that our method improves the LDA classification performance when the withinclass scatter matrix is not only singular but also poorly estimated, with or without a Principal Component Analysis intermediate step and using less linear discriminant features.
Statistical learning approaches for discriminant features selection
 Journal of the Brazilian Computer Society
"... Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sam ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed.
A Simple and Efficient Supervised Method for Spatially Weighted PCA in Face Image Analysis
, 2010
"... Principal Component Analysis (PCA) is an example of a successful unsupervised statistical dimensionality reduction method, especially in small sample size problems. Despite the wellknown attractive properties of PCA, the traditional approach does not incorporate prior information extracted from a s ..."
Abstract
 Add to MetaCart
(Show Context)
Principal Component Analysis (PCA) is an example of a successful unsupervised statistical dimensionality reduction method, especially in small sample size problems. Despite the wellknown attractive properties of PCA, the traditional approach does not incorporate prior information extracted from a specific domain knowledge. The development of techniques that bring together dimensionality reduction and prior knowledge can be performed in the framework of supervised learning methods, like Fisher Discriminant Analysis. Semisupervised methods can also be applied if only a small number of labeled samples is available. In this paper, we propose a simple and efficient supervised method that allows PCA to incorporate explicitly domain knowledge and generates an embedding space that inherits its optimality properties for dimensionality reduction. The method relies on discriminant weights given by separating hyperplanes to generate the spatially weighted PCA. Several experiments using 2D frontal face images and different data sets have been carried out to illustrate the usefulness of the method for dimensionality reduction, classification and interpretation of face images. 1
MODELAGEM E RECONSTRUÇÃO DE IMAGENS DE FACE
"... rio da FEI como parte dos requisitos necessários para a obtenção do título de Mestre em Engenharia Elétrica. São Bernardo do Campo ..."
Abstract
 Add to MetaCart
(Show Context)
rio da FEI como parte dos requisitos necessários para a obtenção do título de Mestre em Engenharia Elétrica. São Bernardo do Campo
A Statistical Discriminant Model for Face Interpretation and Reconstruction
"... Multivariate statistical approaches have played an important role of recognising face images and characterizing their differences. In this paper, we introduce the idea of using a twostage separating hyperplane, here called Statistical Discriminant Model (SDM), to interpret and reconstruct face ima ..."
Abstract
 Add to MetaCart
(Show Context)
Multivariate statistical approaches have played an important role of recognising face images and characterizing their differences. In this paper, we introduce the idea of using a twostage separating hyperplane, here called Statistical Discriminant Model (SDM), to interpret and reconstruct face images. Analogously to the wellknown Active Appearance Model proposed by Cootes et. al, SDM requires a previous alignment of all the images to a common template to minimise variations that are not necessarily related to differences between the faces. However, instead of using landmarks or annotations on the images, SDM is based on the idea of using PCA to reduce the dimensionality of the original images and a maximum uncertainty linear classifier (MLDA) to characterise the most discriminant changes between the groups of images. The experimental results based on frontal face images indicate that the SDM approach provides an intuitive interpretation of the differences between groups, reconstructing characteristics that are very subjective in human beings, such as beauty and happiness. 1.
unknown title
"... Thomaz et al. A maximum uncertainty LDAbased approach… A maximum uncertainty LDAbased approach for limited sample size problems – with application to face recognition ..."
Abstract
 Add to MetaCart
(Show Context)
Thomaz et al. A maximum uncertainty LDAbased approach… A maximum uncertainty LDAbased approach for limited sample size problems – with application to face recognition
Dimensionality Reduction, Classification and Reconstruction Problems in Statistical Learning Approaches
"... Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific subarea of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we rev ..."
Abstract
 Add to MetaCart
(Show Context)
Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific subarea of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we review the theory of such models and compare their separating hypersurfaces for extracting groupdifferences between samples. Classification and reconstruction are the main goals of this comparison. We show recent advances in this topic of research illustrating their application on face and medical image databases. 1