2016
DOI: 10.1007/978-3-319-30298-0_50
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Intrusion Detection System Using PCA and Kernel PCA Methods

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Cited by 21 publications
(13 citation statements)
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“…The aim of the PCA is to reduce the dimensions of the initial variables in the data set while keeping the variance as same as the initial state [15]. To find the best column vector sample to work with, we experimented every possible vector sample using the original NSL-KDD train set with the DNN classifier and found the column vector samples with the highest accuracy score to be 37.…”
Section: Feature Selectionmentioning
confidence: 99%
“…The aim of the PCA is to reduce the dimensions of the initial variables in the data set while keeping the variance as same as the initial state [15]. To find the best column vector sample to work with, we experimented every possible vector sample using the original NSL-KDD train set with the DNN classifier and found the column vector samples with the highest accuracy score to be 37.…”
Section: Feature Selectionmentioning
confidence: 99%
“…Kernel PCA permits for the generation of the PCA to non-linear dimensionality and that can be done using non-linear mapping function. The transformation of samples inputs to high dimensional feature spacing [20]…”
Section: A Mathematical Kpca Algorithmmentioning
confidence: 99%
“…Therefore, the local similarity can be written as a regularization term: 2 2 arg min = Z Z Z -AZ (8) where A is the coefficient matrix which is formed by the similarity weights calculated by (6). We can combine (5) and (8) …”
Section: Fusion Modelmentioning
confidence: 99%
“…The intensity-hue-saturation (IHS) transform [4] and the principal component analysis (PCA) transform [5][6][7] are the methods based on Component Substitution, which transform the interpolated LRMS into a new space obtaining the spatial component that will be replaced by the histogram, matched Pan image. These methods are widely used for pansharpening, because they are easy to implement and fast.…”
Section: Introductionmentioning
confidence: 99%