Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion
نویسندگان
چکیده
We propose an uncorrelated heteroscedastic LDA (UHLDA) technique, which extends the uncorrelated LDA (ULDA) technique by integrating the weighted pairwise Chernoff criterion. The UHLDA can extract discriminatory information present in both the differences between per class means and the differences between per class covariance matrices. Meanwhile, the extracted feature components are statistically uncorrelated the maximum number of which exceeds the limitation of the ULDA. Experimental results demonstrate the promising performance of our proposed technique compared with the ULDA. 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
منابع مشابه
Improving Chernoff criterion for classification by using the filled function
Linear discriminant analysis is a well-known matrix-based dimensionality reduction method. It is a supervised feature extraction method used in two-class classification problems. However, it is incapable of dealing with data in which classes have unequal covariance matrices. Taking this issue, the Chernoff distance is an appropriate criterion to measure distances between distributions. In the p...
متن کاملExtending Kernel Fisher Discriminant Analysis with the Weighted Pairwise Chernoff Criterion
Many linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD) methods are based on the restrictive assumption that the data are homoscedastic. In this paper, we propose a new KFD method called heteroscedastic kernel weighted discriminant analysis (HKWDA) which has several appealing characteristics. First, like all kernel methods, it can handle nonlinearity efficiently in...
متن کاملChernoff Dimensionality Reduction-Where Fisher Meets FKT
Well known linear discriminant analysis (LDA) based on the Fisher criterion is incapable of dealing with heteroscedasticity in data. However, in many practical applications we often encounter heteroscedastic data, i.e., within-class scatter matrices can not be expected to be equal. A technique based on the Chernoff criterion for linear dimensionality reduction has been proposed recently. The te...
متن کاملTwo-Dimensional Heteroscedastic Feature Extraction Technique for Face Recognition
One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with heteroscedastic data. In this paper, we present a novel two-dimensional feature extraction technique for face recognition which is capable of handling the heteroscedastic data in the dataset. The technique is a general form of two-dimensional linear discriminant analysis. It generalizes the interclas...
متن کاملNon-Iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data From Fisher to Chernoff
Linear discriminant analysis (LDA) is a traditional solution to the linear dimension reduction (LDR) problem, which is based on the maximization of the between-class scatter over the within-class scatter. This solution is incapable of dealing with heteroscedastic data in a proper way, because of the implicit assumption that the covariance matrices for all the classes are equal. Hence, discrimin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition
دوره 38 شماره
صفحات -
تاریخ انتشار 2005