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Detecting influential observations in Kernel PCA
Computational Statistics & Data Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michiel Debruyne +2 more
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Stochastic Optimization for Kernel PCA
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Kernel Principal Component Analysis (PCA) is a popular extension of PCA which is able to find nonlinear patterns from data. However, the application of kernel PCA to large-scale problems remains a big challenge, due to its quadratic space complexity and cubic time complexity in the number of examples.
Lijun Zhang 0005 +4 more
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PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images
, 2017 In this chapter an application of PCA, kernel PCA with their modified versions are discussed in the field of dimensionality reduction of hyperspectral images. Hyperspectral image cube is a set of images from hundreds of narrow and contiguous bands of electromagnetic spectrum from visible to near-infrared regions, which usually contains large amount of ...
Aloke Datta, Susmita Ghosh, Ashish Ghosh
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Feature extraction and denoising using kernel PCA
Chemical Engineering Science, 2003 Kernel PCA methodology, an elegant nonlinear generalization of the linear PCA, is illustrated by considering the examples of (i) denoising chaotic time series and, (ii) prediction of properties of polymer nanocomposites developed in our laboratory ...
V K Jayaraman, B D Kulkarni, L Priya
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Denoising by semi-supervised kernel PCA preimaging
Pattern Recognition Letters, 2014 Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality
Lars Kai Hansen
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Kernel PCA for speech enhancement
Interspeech 2011, 2011In this paper, we apply kernel principal component analysis (kPCA), which has been successfully used for image denoising, to speech enhancement. In contrast to other enhancement methods which are based on the magnitude spectrum, we rather apply kPCA to complex spectral data. This is facilitated by Gaussian kernels.
Christina Leitner +2 more
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Novel Kernels and Kernel PCA for Pattern Recognition
2007 International Symposium on Computational Intelligence in Robotics and Automation, 2007Kernel methods are a mathematical tool that provides a generally higher dimensional representation of given data set in feature space for feature recognition and image analysis problems. Typically, the kernel trick is thought of as a method for converting a linear classification learning algorithm into non-linear one, by mapping the original ...
Jason C. Isaacs +2 more
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Feature selection for hypernasality detection using PCA, LDA, kernel PCA and greedy kernel PCA
2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA), 2012Cleft lip and palate, due to morphological problems, allow the passage of air through the nasal cavity, introducing inappropriate nasal resonance during speech production and resulting in hypernasality speech.
E. Belalcazar-Bolanos +6 more
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Classification of soil and vegetation by kernel Fisher and kernel PCA
Pattern Recognition and Image Analysis, 2011Precision Agriculture is concerned with all sort of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e., fertilization,
M. Chapron, G. Bain
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Adaptive robust kernel PCA algorithm
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004A novel algorithm, robust kernel principal component analysis (robust KPCA), is proposed, based on research of the KPCA algorithm and its robustness. This algorithm generalizes the minimum error criteria of signal reconstruction to feature space, which can automatically recognize the outliers in the training sample set, and exterminates their effects ...
Congde Lu +3 more
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