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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Optimally regularised kernel Fisher discriminant classification
TL;DR: Results obtained on real-world and synthetic benchmark datasets indicate that the proposed method is competitive with model selection based on k-fold cross-validation in terms of generalisation, whilst being considerably faster.
31
An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control
TL;DR: The proposed approach is demonstrated on a simulated case study concerning the pressure and level control in the pressurizer of a Pressurized Water Reactor (PWR) and shows the possibility to achieve an adequate control of the process even when a sensor failure occurs.
31
Detecting ROP with Statistical Learning of Program Characteristics
Mohamed Elsabagh,Daniel Barbará,Daniel Fleck,Angelos Stavrou +3 more
- 22 Mar 2017
TL;DR: This paper proposes EigenROP, a novel system to detect ROP payloads based on unsupervised statistical learning of program characteristics based on a novel directional statistics based algorithm to identify deviations from the expected program characteristics during execution.
30
Transcriptional profiling of Arabidopsis thaliana plants’ response to low relative humidity suggests a shoot–root communication
Michal Levin,Nathalie Resnick,Yogev Rosianskey,Igor Kolotilin,Smadar Wininger,Jorge Hugo Lemcoff,Shabtai Cohen,Gadi Galili,Hinanit Koltai,Yoram Kapulnik +9 more
TL;DR: The results suggest that plant roots perceive the low RH stimulus from shoots through a sensing mechanism(s), leading to distinct plant transcriptional responses, potentially reflecting activation of various biological processes.
30
Optimal fusion aided face recognition from visible and thermal face images
TL;DR: Simulation results show that proposed face recognition techniques have significant performance improvement in recognition accuracy suggesting fusion aided face recognition approach that deserves further study and consideration whenever high recognition accuracy is desired.
30
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