Linear dimensionality reduction methods, such as LDA, are often used in object recognition
for feature extraction, but do not address the problem of how to use these features for …

Probabilistic classifiers are developed by assuming generative models which are product
distributions over the original attribute space (as in naive Bayes) or more involved spaces …

Multi-class classifiers often compute scores for the classification samples describing
probabilities to belong to different classes. In order to improve the performance of such …

Classifiers based on probability density estimates can be used to find posterior probabilities
for the objects to be classified. These probabilities can be used for rejection or for combining …

The linear discriminant analysis (LDA) is one of the most traditional linear dimensionality
reduction methods. This paper incorporates the inter-class relationships as relevance …

We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR)
technique for multiclass data. The technique is based on a heteroscedastic two-class …

In the introductory Section 1, the problems of statistical pattern recognition are defined, and
a flow chart is presented to show how a classifier ought to be designed. In Section 2, the …

Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and
classification. LDA in the binaryclass case has been shown to be equivalent to linear …

In this paper, a new learning framework-probabilistic boosting-tree (PBT), is proposed for
learning two-class and multi-class discriminative models. In the learning stage, the …

Linear discriminant analysis (LDA) is a popular statistical approach for dimensionality
reduction. LDA captures the global geometric structure of the data by simultaneously …