Results 11 to 20 of about 26,356,550 (281)
Face recognition using kernel principal component analysis [PDF]
IEEE Signal Processing Letters, 2002 A kernel principal component analysis (PCA) was previously proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This article adopts the kernel PCA as a mechanism for extracting facial features.
Kim, Kwang In +2 more
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Online Kernel Principal Component Analysis: A Reduced-Order Model [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012 Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis. In this paper, we propose an online algorithm for kernel-PCA. To this end, we examine a kernel-based version of Oja's rule, initially put forward to extract a
Honeine, Paul
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Kernel Near Principal Component Analysis [PDF]
, 2002 We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA.
Martin, Shawn B.
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Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G., Soraghan, J.J.
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Comparison Between The Method of Principal Component Analysis And Principal Component Analysis Kernel For Imaging Dimensionality Reduction [PDF]
المجلة العراقية للعلوم الاحصائية, 2019 This paper tackles with two methods to dimensionality reduction, namely principal component analysis (PCA ) in the case of linear combinations and kernel principal component analysis method in the case of nonlinear combinations to digital image ...
Assel Muslim Essa, Asmaa Ghalib Alrawi
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Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning [PDF]
, 2023 Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service
Hira S. Sikandar +9 more
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JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit
BMC Bioinformatics, 2021 Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David +2 more
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Statistical properties of kernel principal component analysis [PDF]
Machine Learning, 2004 The properties of the eigenvalues of Gram matrices are studied in a non-asymptotic setting. Using local Rademacher averages, we provide data-dependent and tight bounds for their convergence towards eigenvalues of the corresponding kernel operator. We perform these computations in a functional analytic framework which allows to deal implicitly with ...
Blanchard, Gilles +2 more
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Hyperspectral image classification based on spectral-spatial kernel principal component analysis network [PDF]
E3S Web of Conferences, 2020 Hyperspectral imagery contains both spectral information and spatial relationships among pixels. How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification.
Fan Yanguo, Hou Shizhe, Yu Dingfeng
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Robust Kernel Principal Component Analysis With ℓ2,1-Regularized Loss Minimization
IEEE Access, 2020 Principal component analysis (PCA) is a widely used unsupervised method for dimensionality reduction. The kernelized version is called kernel principal component analysis (KPCA), which can capture the nonlinear data structure.
Duo Wang, Toshihisa Tanaka
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