NDLI: Novel Kernels and Kernel PCA for Pattern Recognition

Content Provider	IEEE Xplore Digital Library
Author	Isaacs, J.C. Foo, S.Y. Meyer-Baese, A.
Copyright Year	2007
Description	Author affiliation: Florida State Univ., Tallahassee (Isaacs, J.C.; Foo, S.Y.; Meyer-Baese, A.)
Abstract	Kernel methods are a mathematical tool that provides a generally higher dimensional representation of given data set in feature space for feature recognition and image analysis problems. Typically, the kernel trick is thought of as a method for converting a linear classification learning algorithm into non-linear one, by mapping the original observations into a higher-dimensional non-linear space so that linear classification in the new space is equivalent to non-linear classification in the original space. Moreover, optimal kernels can be designed to capture the natural variation present in the data. In this paper we present the performance results of fifteen novel kernel functions and their respective performance for kernel principal component analysis on five select databases. Empirical results show that our kernels perform as well and better than existing kernels on these databases.
Starting Page	438
Ending Page	443
File Size	333898
Page Count	6
File Format	PDF
ISBN	1424407893
DOI	10.1109/CIRA.2007.382927
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Principal component analysis Pattern recognition Databases Extraterrestrial measurements Classification algorithms Computational intelligence Robotics and automation USA Councils Polynomials
Content Type	Text
Resource Type	Article

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