Book Chapter10.1007/BFB0020217
Kernel Principal Component Analysis
Bernhard Schölkopf,Alexander J. Smola,Klaus-Robert Müller +2 more
- 08 Oct 1997
- pp 583-588
2.6K
TL;DR: A new method for performing a nonlinear form of Principal Component Analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
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Abstract: A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
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Citations
Non-fiducial PPG-based authentication for healthcare application
Nima Karimian,Mark Tehranipoor,Domenic Forte +2 more
- 01 Jan 2017
TL;DR: This paper examines, for the first time, non-fiducial feature extraction for photo-plethysmography (PPG) based authentication, which has unique identity properties for human authentication, and is becoming easier to capture by emerging IoT sensors such as MaxFast.
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Local online kernel ridge regression for forecasting of urban travel times
TL;DR: A novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model, which is applied to the forecasting of travel times on London's road network, and is found to outperform three benchmark models in forecasting up to 1 h ahead.
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Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method
Xin Ma,Zhibin Liu +1 more
TL;DR: The results show that the NEA is eligible to describe the nonlinear relationship between the influence factors and the oil production, and it is applicable to make accurate forecasts for the oilProduction in the real applications.
74
Improving aircraft performance using machine learning: a review
Soledad Le Clainche,Esteban Ferrer,Sam Gibson,Elisabeth Cross,Alessandro Parente,Ricardo Vinuesa +5 more
TL;DR: In this article , the state-of-the-art machine learning techniques for aerospace engineering have been reviewed, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring.
Principal Component Analysis: Application to Statistical Process Control
Gilbert Saporta,Ndèye Niang +1 more
- 27 Jan 2010
TL;DR: PCA can also be use d a a multivariate outlier detection method, especially by studying the last p rincipal components, which is useful in multidimensional quality control.
74
References
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
A training algorithm for optimal margin classifiers
Bernhard E. Boser,Isabelle Guyon,Vladimir Vapnik +2 more
- 01 Jul 1992
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
2.8K
Simplified neuron model as a principal component analyzer
TL;DR: A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived.
2.6K