The main purpose of this work is to find an improved regularized covariance estimator of each class with the advantages of LOOC, and BLOOC, which are useful for high dimensional pattern recognition problems. The searc...
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The main purpose of this work is to find an improved regularized covariance estimator of each class with the advantages of LOOC, and BLOOC, which are useful for high dimensional pattern recognition problems. The searching ranges of LOOC and BLOOC are between the linear combinations of three pair covariance estimators. The first proposed covariance estimator (mixed-LOOC1) extended the searching range and is a general case of LOOC and BLOOC. By observing that the optimal value of leave-one-out likelihood function of LOOC usually occurs at near the end point of the parameter domain, the second covariance estimator (mixed-LOOC2), which needs less computation, was proposed. Using the proposed covariance estimator to improve the linear feature extraction methods when the multivariate data are singular or nearly so is demonstrated.
In this paper, a new nonparametric feature extraction method is proposed for high dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least t...
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In this paper, a new nonparametric feature extraction method is proposed for high dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for non-normal data sets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.
An algorithm for computing a triangulated surface which separates a collection of data points that have been segmented into a number of different classes is presented. The problem generalizes the concept of an isosurf...
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An algorithm for computing a triangulated surface which separates a collection of data points that have been segmented into a number of different classes is presented. The problem generalizes the concept of an isosurface which separates data points that have been segmented into only two classes: those for which data function values are above the threshold and those which are below the threshold value. The algorithm is very simple, easy to implement and applies without limit to the number of classes.
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