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Face Hallucination Through KPCA
2009 2nd International Congress on Image and Signal Processing, 2009This paper demonstrates how Kernel Principal Component Analysis (KPCA) can be used for face hallucination. Different with other KPCA-based methods, KPCA in this paper handles samples from two subspaces, namely the high- and low- resolution image spaces.
Yan Liang +4 more
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Gender identification in face images using KPCA
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to extract the feature set of male and female faces.
S. Aji +2 more
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Kernel optimisation for KPCA based on Gaussianity estimation
International Journal of Bio-Inspired Computation, 2014Kernel-based principle component analysis KPCA is an effective feature extraction method. It extends PCA to nonlinear cases using kernel trick. The performance of KPCA relies on the pre-selected parameter of kernel function. In this paper, we propose a kernel parameter optimisation method by using principle component subspace-based Gaussianity ...
Qi Kang 0001 +3 more
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An adaptive KPCA approach for detecting LDoS attack
International Journal of Communication Systems, 2015SummaryLow‐rate denial‐of‐service (LDoS) attack sends out attack packets at low‐average rate of traffic flow in short time. It is stealthier than traditional DoS attack, which makes detection of LDoS extremely difficult. In this paper, an adaptive kernel principal component analysis method is proposed for LDoS attack detection. The network traffic flow
Xiaoyu Zhang 0003 +3 more
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KPCA Plus FDA for Fault Detection
2007Kernel principal component analysis (KPCA) is widely used for fault detection. In this paper, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of the improvements for fault detection performance in terms of high fault detection ...
Peiling Cui, Jiancheng Fang
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Face Recognition Using KPCA and KFDA
Applied Mechanics and Materials, 2013KPCA extracting principal component with nonlinear method is an improved PCA. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. The method of KFDA is equivalent to KPCA plus LDA. KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. The KPCA and
Ren Can Nie, Dong Ming Zhou
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Greedy KPCA in Biomedical Signal Processing
2007Biomedical signals are generally contaminated with artifacts and noise. In case artifacts dominate, the useful signal can easily be extracted with projective subspace techniques. Then, biomedical signals which often represent one dimensional time series, need to be transformed to multi-dimensional signal vectors for the latter techniques to be ...
Ana R. Teixeira +2 more
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Personal Credit Assessment Based on KPCA and SVM
2012 Fifth International Conference on Business Intelligence and Financial Engineering, 2012Personal credit assessment is carried out by setting up a mathematical model to count, calculate and analyze the personal credit data. At present personal credit assessment has already became a kind of worldwide industry. In this paper we combine kernel principal component analysis and support vector machine to propose a new mathematical model based on
Jing Wang +3 more
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The transformer fault diagnosis combing KPCA with PNN
2014 International Joint Conference on Neural Networks (IJCNN), 2014The probabilistic neural network (PNN) can detect the complex relationships and be used to develop its basis for the interpretation of dissolved gas-in-oil data that can identify the fault types. An efficient algorithm known as the kernel principle component analysis (KPCA) is applied to increase features in order to get higher detection accuracy. KPCA
Chenxi Dai, Zhigang Liu 0001, Yan Cui
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A hybrid BGWO with KPCA for intrusion detection
Journal of Experimental & Theoretical Artificial Intelligence, 2019Intrusion detection is the primary model for giving security to the network. There are numerous issues with conventional intrusion detection models (for example, low detection ability against obscu...
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