Kernel discriminant analysis (KDA) is an effective statistical method for dimensionality reduction and feature extraction. However, traditional KDA methods ...
Topics · Kernel Discriminant Analysis · Feature Extraction · Face Recognition Task · Dimensionality Reduction · Face Recognition · High-dimensional Data · Small Sample ...
Kernel discriminant analysis (KDA) is an effective statistical method for dimensionality reduction and feature extraction. However, traditional KDA methods ...
A modification of kernel discriminant analysis for high-dimensional data—with application to face recognition. Dake Zhou, Zhenmin Tang.
A modification of kernel discriminant analysis for high-dimensional data-with application to face recognition. Kernel discriminant analysis (KDA) is an ...
Abstract. When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often ...
In this chapter, we introduce a new kernel discriminant learning method, which attempts to deal with the two problems by using regularization and subspace ...
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In this paper, we propose a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution.
When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often suffer from two ...
Abstract. Many pattern recognition applications involve the treatment of high-dimensional data and the small sample size problem. Principal component.