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WIREs Computational Statistics, 2012
AbstractThe need for classification arises in most scientific pursuits. Typically, there is interest in ‘classifying’ an entity, say, an individual or object, on the basis of some characteristics (feature variables) measured on the entity. This article focuses on the form of classification known as supervised classification or discriminant analysis. It
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AbstractThe need for classification arises in most scientific pursuits. Typically, there is interest in ‘classifying’ an entity, say, an individual or object, on the basis of some characteristics (feature variables) measured on the entity. This article focuses on the form of classification known as supervised classification or discriminant analysis. It
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Subclass discriminant analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006Over the years, many Discriminant Analysis (DA) algorithms have been proposed for the study of high-dimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand).
Manli Zhu, Aleix M. Martínez
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Relational discriminant analysis
Pattern Recognition Letters, 1999Relational discriminant analysis is based on a proximity description of the data. Instead of features, the similarities to a subset of the objects in the training data are used for representation. In this paper we will show that this subset might be small and that its exact choice is of minor importance.
Pekalska, Elzbieta +3 more
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2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Image representation and distance metric are both significant for learning-based visual classification. This paper presents the concept of k-nearest-neighbor simplex (kNNS), which is a simplex with the vertices as the k nearest neighbors of a certain point. kNNS contributes to the image classification problem in two aspects.
Yun Fu 0001 +2 more
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Image representation and distance metric are both significant for learning-based visual classification. This paper presents the concept of k-nearest-neighbor simplex (kNNS), which is a simplex with the vertices as the k nearest neighbors of a certain point. kNNS contributes to the image classification problem in two aspects.
Yun Fu 0001 +2 more
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CRC Critical Reviews in Clinical Laboratory Sciences, 1978
Discriminant analysis (DA) is a pattern recognition technique that has been widely applied in medical studies. It allows multivariate observations ("patterns" or points in multidimensional space) to be allocated to previously defined groups (diagnostic categories).
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Discriminant analysis (DA) is a pattern recognition technique that has been widely applied in medical studies. It allows multivariate observations ("patterns" or points in multidimensional space) to be allocated to previously defined groups (diagnostic categories).
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Convolutional Discriminant Analysis
2018 24th International Conference on Pattern Recognition (ICPR), 2018Softmax regressor is arguably the most commonly used classifier in convolutional neural networks (CNNs). However, the cross-entropy based softmax loss only supervises the deep neural networks to learn effective representations of data, but does not explicitly enforce the separability between the classes.
Guoqiang Zhong 0001 +4 more
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Archives of Pediatrics & Adolescent Medicine, 1984
Discriminant analysis (DA) is a family of statistical methods in which the primary purpose is to predict into which of several discrete groups (eg, diagnostic categories) an individual subject should be classed. This might be called the classification or forecasting function. A second use of DA examines how well the derived discrimination equation fits
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Discriminant analysis (DA) is a family of statistical methods in which the primary purpose is to predict into which of several discrete groups (eg, diagnostic categories) an individual subject should be classed. This might be called the classification or forecasting function. A second use of DA examines how well the derived discrimination equation fits
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IEEE Transactions on Neural Networks, 2000
In this article the role of the bootstrap is highlighted for nonlinear discriminant analysis using a feedforward neural network model. Statistical techniques are formulated in terms of the principle of the likelihood of a neural-network model when the data consist of ungrouped binary responses and a set of predictor variables.
Masaaki Tsujitani, Takashi Koshimizu
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In this article the role of the bootstrap is highlighted for nonlinear discriminant analysis using a feedforward neural network model. Statistical techniques are formulated in terms of the principle of the likelihood of a neural-network model when the data consist of ungrouped binary responses and a set of predictor variables.
Masaaki Tsujitani, Takashi Koshimizu
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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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