Book Chapter10.1007/BFB0020217
Kernel Principal Component Analysis
Bernhard Schölkopf,Alexander J. Smola,Klaus-Robert Müller +2 more
- 08 Oct 1997
- pp 583-588
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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
•Dissertation
Spatio-temporal forecasting of network data
James Haworth
- 28 Aug 2014
TL;DR: A local online kernel ridge regression model is developed that outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network.
19
Local proper generalized decomposition
TL;DR: An extension of local models in a PGD—and thus, a priori—context is presented and no gluing or special technique is needed to deal with the resulting set of local reduced‐order models, in contrast to most proper orthogonal decomposition local approximations.
A comparative study of linear and nonlinear anomaly detectors for hyperspectral imagery
Hirsh Goldberg,Nasser M. Nasrabadi +1 more
- 07 May 2007
TL;DR: In this article, a dual window technique is used to separate the local area around each pixel into two regions - aninner-window region (IWR) and an outer-window regions (OWR), and pixel spectra from each region are projected into a subspace which is defined by projection bases that can be generated in several ways.
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3D face recognition using kernel-based PCA approach
Marcella Peter,Jacey-Lynn Minoi,Irwandi Hipiny +2 more
- 01 Jan 2019
TL;DR: The results of the recognition rates have shown that the kernel method outperformed the standard PCA and non-linear PCA in 3D face recognition.
19
•Proceedings Article
Efficient Feature Embeddings for Student Classification with Variational Auto-Encoders.
Severin Klingler,Rafael Wampfler,Tanja Käser,Barbara Solenthaler,Markus Gross +4 more
- 01 Jun 2017
TL;DR: A semi-supervised classification pipeline that makes effective use of unlabeled data to significantly improve model quality and outperforms previous methods for finding efficient feature embeddings and generalizes better to imbalanced data sets compared to expert features.
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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