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
On Predicting Relapse in Schizophrenia using Mobile Sensing in a Randomized Control Trial
Rui Wang,Weichen Wang,Mikio Obuchi,Emily A. Scherer,Rachel Brian,Dror Ben-Zeev,Tanzeem Choudhury,John M. Kane,Martar Hauser,Megan Walsh,Andrew T. Campbell +10 more
- 23 Mar 2020
TL;DR: This work uses the CrossCheck study dataset to develop methods to predict whether or not a patient with schizophrenia is going to relapse from mobile phone data and finds the best relapse prediction result using the first 100 principal components from both passive sensing and self-reports with 30-day prediction windows.
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A Selective Kernel PCA Algorithm for Anomaly Detection in Hyperspectral Imagery
Yanfeng Gu,Ying Liu,Ye Zhang +2 more
- 14 May 2006
TL;DR: The proposed algorithm tries to solve the problem brought by high dimensionality of hyperspectral images in anomaly detection by performing kernel principal component analysis on original data to fully mine high-order correlation between spectral bands.
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Testing the Suitability of Automated Machine Learning for Weeds Identification
TL;DR: It can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.
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Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection
TL;DR: In this paper , a combination of WGAN and encoder CNN, adapted from f-AnoGAN, was used for on-line anomaly detection in additive manufacturing for automotive components, and the results showed that using clustering methods with features generated by the discriminator yields better results than computing an anomaly score solely.
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Comparison on PPCA, KPPCA and MPPCA Based Missing Data Imputing for Traffic Flow
Yuebiao Li,Zhiheng Li,Li Li,Yi Zhang,Maojing Jin +4 more
- 03 Jul 2013
TL;DR: The possibility of applying more complex PPCA methods, e.g. Kernel Probabilistic Principal Component Analysis (KPPCA) or Mixed Probabilism Principal Component analysis (MPPCA), to impute missing data is explored and test results show that the basic P PCA method is still the first choice in missing data imputing for traffic flow for online systems.
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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.
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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