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
Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method
TL;DR: A machine learning model for the prediction of the magnetization dynamics as function of the external field described by the Landau-Lifschitz-Gilbert equation, the partial differential equation of motion in micromagnetism is established.
8
Modal Strain Energy-Based Debonding Assessment of Sandwich Panels Using a Linear Approximation with Maximum Entropy
TL;DR: A damage assessment algorithm to localize and quantify debonding in sandwich panels using damage indices derived from the modal strain energy method and a linear approximation with a maximum entropy algorithm is presented.
8
Survey of intelligent surveillance system for monitoring international border security
TL;DR: In this paper, an automated vehicle with a monitoring system on the basis of face recognition and detection algorithms, the human presence is checked via the system which after that runs the algorithm for face recognition.
8
A complete person re-identification model using Kernel-PCA-based Gabor-filtered hybrid descriptors
P. K. Sathish,S. Balaji +1 more
TL;DR: Major contributions of this work are to detect pedestrians from surveillance videos using CNN-based learning and to generate a kernel-PCA-based spatial descriptor and evaluate the descriptor using known distance metric learning methods on benchmark datasets.
8
Data-Driven Geometric Design Space Exploration and Design Synthesis
Wei Chen
- 01 Jan 2019
TL;DR: This dissertation introduces ways of capturing a compact representation that describes the variability of designs, so that the authors can synthesize designs and explore design options using this compact representation instead of the original high-dimensional design variables.
8
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