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Relative Principle Component and Relative Principle Component Analysis Algorithm.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Rangan, C. Pandu
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Derong Liu
Shumin Fei
Zengguang Hou
Huaguang Zhang
Changyin Sun
Source :
Advances in Neural Networks: ISNN 2007; 2007, p985-993, 9p
Publication Year :
2007

Abstract

Aiming at the problems happened in the practical application of traditional Principle Component Analysis (PCA), the concept of Relative Principle Component (RPC) and method of Relative Principle Component Analysis (RPCA) are put forward. Meanwhile, some concepts such as Relative Transform (RT), "Rotundity" Scatter and so on are introduced. The new algorithm can overcome some disadvantages of traditional PCA for compressing data when data is "Rotundity" Scatter. A simulation has been used to demonstrate the effectiveness and practicability of the algorithm proposed. The RPCs selected by RPCA are more representative, and the way to choose RPCs is more flexible, so that the application of the new algorithm will be very extensive. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540723929
Database :
Complementary Index
Journal :
Advances in Neural Networks: ISNN 2007
Publication Type :
Book
Accession number :
33198879
Full Text :
https://doi.org/10.1007/978-3-540-72393-6_117