Journal ArticleDOI
On the Discriminant Vector Method of Feature Selection
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TLDR
The correspondence discusses the relationship of the discriminant vector method of feature selection and the method of Kittler and Young, showing that the latter method is, from the point of view of dimensionality reduction, more powerful and also computationally more efficient.Abstract:
The correspondence discusses the relationship of the discriminant vector method of feature selection [1] and the method of Kittler and Young [5]. Although both methods determine the feature space coordinate axes by maximizing the generalized Fisher criterion of discriminatory power, with the exception of two class case the resulting feature spaces are considerably different because of the difference in the constraints imposed on the axes by individual methods. It is shown that the latter method is, from the point of view of dimensionality reduction, more powerful and also computationally more efficient.read more
Citations
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Journal ArticleDOI
A generalized Foley-Sammon transform based on generalized fisher discriminant criterion and its application to face recognition
TL;DR: The conception of the generalized Fisher discriminant criterion is presented, the generalized Foley-Sammon transform (GFST) is proposed, and the experimental results show that present method is superior to the existing methods in terms of correct classification rate.
Journal ArticleDOI
A theorem on the uncorrelated optimal discriminant vectors
TL;DR: It is proved that the classical optimal discriminant vectors are equivalent to UODV, which can be used to extract (L−1) uncorrelated discriminant features for L-class problems without losing any discriminant information in the meaning of Fisher discriminant criterion function.
Journal ArticleDOI
A generalized optimal set of discriminant vectors
TL;DR: The experimental results show that the present method is superior to the Foley-Sammon method, the positive pseudoinverse method, and the matrix rank decomposition method in terms of correct classification rate.
Journal ArticleDOI
On an Extended Fisher Criterion for Feature Selection
TL;DR: This correspondence considers the extraction of features as a task of linear transformation of an initial pattern space into a new space, optimal with respect to discriminating the data.
Journal ArticleDOI
Recent Developments in Pattern Recognition
TL;DR: A very brief survey of recent developments in basic pattern recognition techniques is presented.
References
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Journal ArticleDOI
Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
TL;DR: A method is developed herein to use the Karhunen-Loeve expansion to extract features relevant to classification of a sample taken from one of two pattern classes.
Journal ArticleDOI
An Optimal Set of Discriminant Vectors
Donald H. Foley,John W. Sammon +1 more
TL;DR: A new method for the extraction of features in a two-class pattern recognition problem is derived that is based entirely upon discrimination or separability as opposed to the more common approach of fitting.
Journal ArticleDOI
A new approach to feature selection based on the Karhunen-Loeve expansion
Josef Kittler,Peter C. Young +1 more
TL;DR: A new K-L technique is described that overcomes some of the limitations of the earlier procedures and is suggested that it is particularly useful for pattern recognition when combined with classification procedures based upon discriminant functions obtained by recursive least squares analysis.
Journal ArticleDOI
On the generalized Karhunen-Loeve expansion (Corresp.)
Y. T. Chien,King-Sun Fu +1 more
TL;DR: The purpose of this correspondence is to show that the cited optimum properties can be retained by defining a generalized Karhunen-Loeve expansion which considers the possibility of two or more stochastic processes generating the random functions.