Results 41 to 50 of about 192,804 (181)
EURASIP Journal on Advances in Signal Processing, 2009 Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines.
Mathieu Fauvel +2 more
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Kernal principal component analysis of the ear morphology [PDF]
, 2017 This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis ...
Epain, Nicolas +4 more
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Streaming Kernel Principal Component Analysis
, 2015 Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time ...
Ghashami, Mina +2 more
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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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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.
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Remote Sensing, 2019 Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA ...
Lan Zhang, Hongjun Su, Jingwei Shen
doaj +1 more source
Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis [PDF]
, 2012 GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary.
Chen, Feng +5 more
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Applied Sciences, 2021 The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts.
Elena Quatrini +4 more
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A similarity study of I/O traces via string kernels [PDF]
, 2018 Understanding I/O for data-intense applications is the foundation for the optimization of these applications. The classification of the applications according to the expressed I/O access pattern eases the analysis.
Dolz, Manuel F. +3 more
core +1 more source
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
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