Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
|Title||2012 3rd International Workshop on Cognitive Information Processing (CIP)|
|Number of pages||6|
|Workshop||3rd International Workshop on Cognitive Information Processing (CIP)|
|Period||28/05/12 → 30/05/12|
|Citations||Web of Science® Times Cited: No match on DOI|
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