Conference paper
A Hold-out method to correct PCA variance inflation
In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure was introduced.
We propose a Hold-out procedure whose computational cost is lower and, unlike the LOO method, the number of SVD's does not scale with the sample size. We analyze its properties from a theoretical and empirical point of view. Finally we apply it to a real classification scenario.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2012 |
| Pages: | 1-6 |
| Proceedings: | 3rd International Workshop on Cognitive Information Processing (CIP) |
| ISBN: | 1467318779 , 9781467318778 , 1467318760 , 1467318787 , 9781467318761 and 9781467318785 |
| Types: | Conference paper |
| DOI: | 10.1109/CIP.2012.6232926 |
| ORCIDs: | Hansen, Lars Kai |
Approximation methods Computational efficiency Conferences LOO method LOO procedure Mathematical model PCA algorithm PCA variance inflation Principal component analysis SVD Standards Training classification scenario computational complexity computational cost correction method hold-out method hold-out procedure leave-one-out procedure principal component analysis singular value decomposition
