Abstract
We propose kernel Parallel Analysis (kPA) for
automatic kernel scale and model order selection in Gaussian
kernel PCA. Parallel Analysis [1] is based on a permutation
test for covariance and has previously been applied for model
order selection in linear PCA, we here augment the procedure
to also tune the Gaussian kernel scale of radial basis function
based kernel PCA.We evaluate kPA for denoising of simulated
data and the US Postal data set of handwritten digits. We find
that kPA outperforms other heuristics to choose the model
order and kernel scale in terms of signal-to-noise ratio (SNR)
of the denoised data.
Original language | English |
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Journal | I E E E Transactions on Neural Networks and Learning Systems |
Volume | 23 |
Issue number | 1 |
Pages (from-to) | 163-168 |
ISSN | 1045-9227 |
DOIs | |
Publication status | Published - 2012 |
Keywords
- Kernel principal component analysis
- Denoising
- Parallel analysis
- Model selection