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- W2100039146 abstract "The task of studying the properties of conflgurations of points embeddedin a metric space has long been a central task in pattern recognition, buthas acquired even greater importance after the recent introduction of kernel-based learning methods. These methods work by virtually embedding generaltypes of data in a vector space, and then analyzing the properties of theresulting data cloud. While a number of techniques for this task have beendeveloped in flelds as diverse as multivariate statistics, neural networks, andsignal processing, many of them show an underlying unity. In this chapterwe describe a large class of pattern analysis methods based on the use ofgeneralized eigenproblems, which reduce to solving the equation Aw =" @default.
- W2100039146 created "2016-06-24" @default.
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- W2100039146 date "2005-01-01" @default.
- W2100039146 modified "2023-10-18" @default.
- W2100039146 title "Eigenproblems in Pattern Recognition" @default.
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- W2100039146 doi "https://doi.org/10.1007/3-540-28247-5_5" @default.
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