A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection

Kuang, Fangjun; Zhang, Siyang; Jin, Zhong; Wang, Xu

doi:10.1007/s00500-014-1332-7

Cited by 115 publications

(60 citation statements)

References 21 publications

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“…KPCA (Kuang et al, ) performs a nonlinear PCA in the transformed linear space through a kernel function

Ψ (boldx, boldy) = (φ (x), φ (y))

that essentially sets up a nonlinear mapping

φ (x)

from the original space to the feature space to attain the optimum solution. The

h

th principal component

Q^{true(h true)}

can be measured via the projection from a point in the feature space:

Q^{{true(h true)}^{T}} φ true(x true) = true(\sum_{i = 1}^{D} ρ_{i}^{true(h true)} φ (x_{i}), φ true(x true) true),

where

D

is the number of instances in the dataset and

ρ^{h}

is the

h

th eigenvector.…”

Section: Methodsmentioning

confidence: 99%

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Lin

Jiang

et al. 2019

Natural Resource Modeling

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The kernel‐based statistical semantic topic model is introduced for comprehending three species of internationally important Ramsar wetland documents describing the Lashi Lake wetland in the Yunnan Province, the Yancheng wetland in the Jiangsu Province, and the Zoige wetland in the Sichuan Province of China. Latent Dirichlet allocation (LDA) features are used to represent the semantic components of wetland documents. Kernel principal component analysis (KPCA) maps the topic components to the kernel space to attain the low dimensional principal components. Support vector machines (SVMs) are used to comprehend the semantic distribution of distinct wetland documents in the kernel space. The LDA+KPCA+SVM algorithm reaches 77.0% training and 75.9% test accuracy and 0.902 training and 0.840 test mean average precision scores in the application of comprehending the wetland documents, respectively. The performance of the proposed kernel‐based model is superior to the traditional models of LDA+SVM and LDA+PCA+SVM.

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“…KPCA (Kuang et al, ) performs a nonlinear PCA in the transformed linear space through a kernel function

Ψ (boldx, boldy) = (φ (x), φ (y))

that essentially sets up a nonlinear mapping

φ (x)

from the original space to the feature space to attain the optimum solution. The

h

th principal component

Q^{true(h true)}

can be measured via the projection from a point in the feature space:

Q^{{true(h true)}^{T}} φ true(x true) = true(\sum_{i = 1}^{D} ρ_{i}^{true(h true)} φ (x_{i}), φ true(x true) true),

where

D

is the number of instances in the dataset and

ρ^{h}

is the

h

th eigenvector.…”

Section: Methodsmentioning

confidence: 99%

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Lin

Jiang

et al. 2019

Natural Resource Modeling

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show abstract

“…SVMs can learn a pattern of large datasets and can scale better because the complexity of the SVM classification does not depend on the dimensions of the features. SVMs also has the ability to dynamically update the training pattern every time there is a new pattern for the classification [14].…”

Section: Support Vector Machinementioning

confidence: 99%

Performance comparison of intrusion detection system based anomaly detection using artificial neural network and support vector machine

Cahyo

Hidayat

Adhipta

2016

AIP Conference Proceedings

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“…By performing this nonlinear mapping, it is expected that the complex distribution of face patterns becomes linearly separable in the kernel feature space. Following the success of applying the kernel trick in support vector machines (SVMs) (Ha et al 2013;Kuang et al 2015), many kernel-based PCA/LDA methods have been developed and applied in pattern recognition tasks, such as kernel PCA (KPCA) (Schölkopf et al 1998), kernel Fisher discriminant (KFD) , generalized discriminant analysis (GDA) (Baudat and Anouar 2000), and kernel direct LDA (KDDA) (Lu et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Fisher discriminant analysis based on kernel cuboid for face recognition

Liu

Zhang

2015

Soft Comput

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This paper builds the concept of kernel cuboid, and proposes a new kernel-based image feature extraction method for face recognition. The proposed method deals with a face image in a block-wise manner, and independently performs kernel discriminant analysis in every block set, using kernel cuboid instead of kernel matrix. Experimental results on the ORL and UMIST face databases show the effectiveness and scalability of the proposed method.

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A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection

Cited by 115 publications

References 21 publications

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Performance comparison of intrusion detection system based anomaly detection using artificial neural network and support vector machine

Fisher discriminant analysis based on kernel cuboid for face recognition

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