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
A Biophysical 3D Morphable Model of Face Appearance
Sarah Alotaibi,William A. P. Smith +1 more
- 01 Oct 2017
TL;DR: A two parameter spectral model of skin colouration is presented, methods for fitting the model to data captured in a lightstage and then built on a sample of such registered data, and face editing results are presented.
•Posted Content
Contrastive Principal Component Analysis
TL;DR: This work conducts a wide variety of experiments in which cPCA identifies important dataset-specific patterns that are missed by PCA, demonstrating that it is useful for many applications: subgroup discovery, visualizing trends, feature selection, denoising, and data-dependent standardization.
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Video object matching across multiple non-overlapping camera views based on multi-feature fusion and incremental learning
Huiyan Wang,Xun Wang,Jia Zheng,John R. Deller,Haoyu Peng,Leqing Zhu,Wei-Gang Chen,Xiaolan Li,Liu Riji,Hujun Bao +9 more
TL;DR: A novel framework based on multi-feature fusion and incremental learning to match the objects across disjoint views in the absence of space–time cues is proposed and can be used in real-time video surveillance applications.
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Online feature extraction based on accelerated kernel principal component analysis for data stream
TL;DR: An incremental type of KPCA that can update an eigen-space incrementally for a sequence of data is proposed called Chunk IKPCA (CIKPCA) where a chunk of multiple data is learned with single eigenvalue decomposition to reduce the computational costs in learning chunk data.
Statistical Methods for Outlier Detection in Space Telemetries
Clémentine Barreyre,Loic Boussouf,Bertrand Cabon,Béatrice Laurent,Jean-Michel Loubes +4 more
- 28 May 2018
TL;DR: This paper sets up features based on fixed functional bases and databased bases and applies outlier detection methods on those features, which can be distance- or density-based and tested on real telemetry data.
21
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.
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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