Kpca Gögüslü
Kpca Gögüslü
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The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM.
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The data can be passed to the kpca function in a matrix or a data.frame, in addition kpca also supports input in the form of a kernel matrix of class kernelMatrix or as a list of character vectors where a string kernel has to be used.
Network intrusion detection identifies malicious activity in the network by analyzing the behavior of network traffic. As an important part of network intrusion detection, feature extraction plays a crucial role in improving the performance of intrusion detection. This research proposes a novel secondary feature extraction method called L-KPCA based on the Liner Discriminant Analysis (LDA) and ...
Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data.
In particular, we discuss a simple method that can be used to approximate PCA in the latter case. This method can also help approximate kernel PCA or kernel PCA (KPCA) for a large-scale dataset. We hope this manuscript will give readers a solid foundation on PCA, approximate PCA, and approximate KPCA.
Rapid and accurate threat evaluation(TE) of incoming targets is the key part in air defense. In order to evaluate the target threat effectively, a threat evaluation index system is constructed. On this basis, an improved kernel principal component analysis method (KPCA) based on hybrid kernel function is proposed to realize dimension reduction of the index data, particle swarm optimization ...
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The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation.
An efficient reliability method is presented to address the challenge inherent in the high-dimensional reliability analysis. The critical contribution is an elegant implementation of combining the kernel principal component analysis (KPCA)-based nonlinear dimension reduction and the Gaussian process regression (GPR) surrogate model by introducing a nonintrusive, joint-training scheme. This ...
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Gas-insulated Switchgear Discharge Pattern Recognition Based on KPCA and Multilayer Neural Network Identifying the discharge type based on partial discharge signals in Gas-insulated Switchgear (GIS) equipment is very important for fault diagnosis and early warning of GIS equipment.
Fracture is the main pore space for volcanic reservoir, serving as the controlling factor of reservoir productivity. Conventional well logging data often fail to fracture characterization and classification in volcanic reservoir since the degree or extent of the fracture development varies in scales in different locations. A method for fracture developing degree discrimination, based on a ...
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