[1]YAN Xiaobo,WANG Shitong,GUO Huiling.Feature reduction of high order statistics based on Parzen window[J].CAAI Transactions on Intelligent Systems,2013,8(1):1-10.[doi:10.3969/j.issn.1673-4785.201210046]
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Feature reduction of high order statistics based on Parzen window

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 1 Page number: 1-10 Column: 学术论文—人工智能基础 Public date: 2013-03-25
Title:
Feature reduction of high order statistics based on Parzen window
Author(s):
YAN Xiaobo;  WANG Shitong;  GUO Huiling
School of Digital Media, Jiangnan University, Wuxi 214122, China
Keywords:
KCCA;  higher-order statistics;  Parzen window;  feature reduction
CLC:
TP181
DOI:
10.3969/j.issn.1673-4785.201210046
Abstract:
The high order statistics method can often extract more information regarding original data than a low order statistics; yet in the meantime create higher time complexity. The high order statistics methods were constructed by utilizing estimation based on Parzen window. It was revealed that the kernel covariance component analysis (KCCA) method proposed earlier by the researchers, contained useful information on the high order statistics and could be obtained by only adjusting the parameters of the proposed generalized D vs E. Also based on the second order statistics, the heavy computational burden about the highorder statistics can be avoided. That is to say, the KCCA method can accomplish the feature reduction of high order statistics without increasing its computational complexity.
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