Journal article
A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis
Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA.
As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen.
Language: | English |
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Year: | 2011 |
Pages: | 2027-2044 |
ISSN: | 15337928 and 15324435 |
Types: | Journal article |
ORCIDs: | Hansen, Lars Kai |