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Evolutionary discriminant analysis
IEEE Transactions on Evolutionary Computation, 2006An evolutionary approach to the supervised reduction of dimensions is introduced in this paper. Traditionally, such reduction has been accomplished by maximizing one or another measure of class separation. Quite often, the rank deficiency of the involved covariance matrices precludes the application of this classical approach to real situations ...
Alejandro Pazos Sierra +1 more
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18th International Conference on Pattern Recognition (ICPR'06), 2006
The main objective of the work presented here is to introduce a supervised, nonlinear dimensionality reduction technique which, performs well-known linear discriminant analysis in a local way and which is able to provide a powerful mapping with less computational effort than other nonlinear reduction methods.
Marco Loog, Dick de Ridder
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The main objective of the work presented here is to introduce a supervised, nonlinear dimensionality reduction technique which, performs well-known linear discriminant analysis in a local way and which is able to provide a powerful mapping with less computational effort than other nonlinear reduction methods.
Marco Loog, Dick de Ridder
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1993
Statistical Discriminant Analysis is a classical technique in pattern matching with applications for classification problems and more general decision tasks. In this paper, we use a specific class of discriminant functions which we call product discriminant functions, or simply PDF's.
Jorge Ricardo Cuellar, Hans Ulrich Simon
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Statistical Discriminant Analysis is a classical technique in pattern matching with applications for classification problems and more general decision tasks. In this paper, we use a specific class of discriminant functions which we call product discriminant functions, or simply PDF's.
Jorge Ricardo Cuellar, Hans Ulrich Simon
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2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010
Linear Discriminant Analysis (LDA) is a popular tool for multiclass discriminative dimensionality reduction. However, LDA suffers from two major problems: (1) It only optimizes the Bayes error for the case of unimodal Gaussian classes with equal covariances (assuming full rank matrices) and, (2) The multiclass extension maximizes the sum of pairwise ...
Karim T. Abou-Moustafa +2 more
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Linear Discriminant Analysis (LDA) is a popular tool for multiclass discriminative dimensionality reduction. However, LDA suffers from two major problems: (1) It only optimizes the Bayes error for the case of unimodal Gaussian classes with equal covariances (assuming full rank matrices) and, (2) The multiclass extension maximizes the sum of pairwise ...
Karim T. Abou-Moustafa +2 more
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2008 19th International Conference on Pattern Recognition, 2008
In this paper, we propose a non-parametric discriminant analysis method (no assumption on the distributions of classes), called Parzen discriminant analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is ...
Youhan Fang +4 more
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In this paper, we propose a non-parametric discriminant analysis method (no assumption on the distributions of classes), called Parzen discriminant analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is ...
Youhan Fang +4 more
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Fisher Discriminant Analysis and Factor Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983We show that information on the inherent structure of multidimensional data derived from a factor analysis procedure is equivalent to information obtained by Fisher discriminant analysis techniques, provided certain conditions, usually required in the factor analysis model, are satisfied.
Giacomo Della Riccia +1 more
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Multi-View Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for ...
Meina Kan +4 more
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DDoS discrimination by Linear Discriminant Analysis (LDA)
2012 International Conference on Computing, Networking and Communications (ICNC), 2012In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet.
Theerasak Thapngam +2 more
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A nonlinear discriminant analysis
Computer Programs in Biomedicine, 1971Abstract Two linkable computer programs have been developed for a special case of nonlinear discriminant analysis. Here the discriminant formula is nonlinear because joint normal distributions are postulated, but not equal covariance matrices (abbr. CV-matrices).
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Semisupervised Generalized Discriminant Analysis
IEEE Transactions on Neural Networks, 2011Generalized discriminant analysis (GDA) is a commonly used method for dimensionality reduction. In its general form, it seeks a nonlinear projection that simultaneously maximizes the between-class dissimilarity and minimizes the within-class dissimilarity to increase class separability.
Yu Zhang 0006, Dit-Yan Yeung
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