Kernel discriminant analysis for regression problems
نویسندگان
چکیده
منابع مشابه
Kernel discriminant analysis for regression problems
In this paper, we propose a nonlinear feature extraction method for regression problems to reduce the dimensionality of the input space. Previously, a feature extraction method LDAr, a regressional version of the linear discriminant analysis, was proposed. In this paper, LDAr is generalized to a non-linear discriminant analysis by using the so called kernel trick. The basic idea is to map the i...
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Linear Discriminant Analysis (LDA) and its nonlinear version Kernel Discriminant Analysis (KDA) are well-known and widely used techniques for supervised feature extraction and dimensionality reduction. They determine an optimal discriminant space for (non)linear data projection based on certain assumptions, e.g. on using normal distributions (either on the input or in the kernel space) for each...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2012
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2011.11.006