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» Robust principal component analysis
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NECO
1998
151views more  NECO 1998»
13 years 7 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
ICASSP
2011
IEEE
12 years 11 months ago
Feature selection through gravitational search algorithm
In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimi...
João Paulo Papa, Andre Pagnin, Silvana Arti...
NIPS
2000
13 years 9 months ago
Automatic Choice of Dimensionality for PCA
A central issue in principal component analysis (PCA) is choosing the number of principal components to be retained. By interpreting PCA as density estimation, this paper shows ho...
Thomas P. Minka
JASIS
2006
90views more  JASIS 2006»
13 years 7 months ago
Can scientific journals be classified in terms of aggregated journal-journal citation relations using the Journal Citation Repor
The aggregated citation relations among journals included in the Science Citation Index provide us with a huge matrix which can be analyzed in various ways. Using principal compon...
Loet Leydesdorff
IMC
2005
ACM
14 years 1 months ago
Network Anomography
Anomaly detection is a first and important step needed to respond to unexpected problems and to assure high performance and security in IP networks. We introduce a framework and ...
Yin Zhang, Zihui Ge, Albert G. Greenberg, Matthew ...