The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
نویسندگان
چکیده
منابع مشابه
Kernel Principal Component Analysis
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal components in high{ dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d{pixel products in images. We give the derivation of the method and present experimenta...
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Article history: Received 15 December 2011 Received in revised form 28 June 2013 Accepted 5 July 2013 Available online 16 July 2013
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Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture nonlinear structures within large data sets, and is a central tool in data analysis and learning. To allow for nonlinear relations, typically a full n ⇥ n kernel matrix is constructed over n data points, but this requires too much space and time for large values of n. Techniques such as the Nyström ...
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ژورنال
عنوان ژورنال: International Journal of Oil, Gas and Coal Engineering
سال: 2019
ISSN: 2376-7669
DOI: 10.11648/j.ogce.20190701.11