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
Subspace-based gearbox condition monitoring by kernel principal component analysis
TL;DR: Experimental analysis with a fatigue test of an automobile transmission gearbox shows that the KPCA features outperform PCA features in terms of clustering capability, and both the two K PCA-based subspace methods can be effectively applied to gearbox condition monitoring.
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Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
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TL;DR: An industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance is proposed, expanding on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks.
ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm
Jalal Aldin Nasiri,Mahmoud Naghibzadeh,H. Sadoghi Yazdi,Bahram Naghibzadeh +3 more
- 25 Nov 2009
TL;DR: Experimental results demonstrate that the approach adopted better classifies ECG signals, and four types of arrhythmias were distinguished with 93% accuracy.
Cross-Project and Within-Project Semisupervised Software Defect Prediction: A Unified Approach
TL;DR: A unified and effective solution for both CSDP and WSDP problems is provided and a cost-sensitive kernelized semisupervised dictionary learning (CKSDL) approach is proposed that outperforms state-of-the-art WSDP methods, using unlabeled cross-project defect data can help improve the WSDP performance, and CKSDL generally obtains significantly better prediction performance than related SSDP methods in the CSDP scenario.
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Kernel-driven Similarity Learning
TL;DR: Comprehensive experimental evaluations of the proposed multiple kernel-based learning method demonstrate its superior performance compared to other state-of-the-art methods on clustering and recommender systems and shows the great potential of the model for other possible applications.
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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.
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