Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …

When applied to high-dimensional pattern classification tasks such as face recognition,
traditional kernel discriminant analysis methods often suffer from two problems:(1) small …

Kernel-based learning methods have been widely used in various machine learning tasks
such as dimensionality reduction, classification and regression. Because the performance of …

In this paper, the method of kernel direct discriminant analysis is analyzed from a new
viewpoint and its theoretical foundation is revealed. Based on this result, an efficient and …

In this paper, we propose a new kernel discriminant analysis called kernel relevance
weighted discriminant analysis (KRWDA) which has several interesting characteristics. First …

It is well known that there exist two fundamental limitations in the linear discriminant analysis
(LDA). One is that it cannot be applied when the within-class scatter matrix is singular, which …

Feature extraction is among the most important problems in face recognition systems. In this
paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kernel …

Kernel discriminant analysis (KDA) is a widely used approach in feature extraction
problems. However, for high-dimensional multi-class tasks, such as faces recognition …

The selection of kernel function and its parameter influences the performance of kernel
learning machine. The difference geometry structure of the empirical feature space is …

Kernel discriminant analysis (KDA) is a widely used tool in feature extraction community.
However, for high-dimensional multi-class tasks such as face recognition, traditional KDA …