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103
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NIPS
2008
15 years 2 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
IJON
2006
15 years 16 days ago
2006
We present an application of Independent Component Analysis (ICA) to the discrimination of mental tasks for EEG-based Brain Computer Interface systems. ICA is most commonly used w...
98
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PAMI
2008
15 years 14 days ago
2008
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
92
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IJON
2007
15 years 13 days ago
2007
In this paper we present a method of parameter optimization, relative trust-region learning, where the trust-region method and the relative optimization [21] are jointly exploited...
82
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ICASSP
2011
IEEE
14 years 4 months ago
2011
IEEE
The motivation for this study is the need for careful analysis of aperiodicity of the excitation component in expressive voices. The paper proposes analysis methods which can pres...