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
•Posted Content
Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity
TL;DR: Three main areas where decision tree methods could be improved are identified: (i) learning over high dimensional output spaces, (ii) learning with large sample datasets and stringent memory constraints at prediction time and (iii) learningover high dimensional sparse input spaces.
11
Feature extraction and health status prediction in PV systems
Edgar Hernando Sepúlveda Oviedo,Louise Travé-Massuyès,Audine Subias,Corinne Alonso,Marko Pavlov +4 more
TL;DR: In this paper , an approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage and a nonlinear regression method named partial least squares, which is then combined with linear discriminant analysis and compared.
•Posted Content
Nonparametric Independence Testing for Small Sample Sizes
Aaditya Ramdas,Leila Wehbe +1 more
TL;DR: This work provides strong empirical evidence that by employing shrunk operators when the sample size is small, one can attain an improvement in power at low false positive rates and is important for more powerful nonparametric detection of subtle nonlinear dependencies for small samples.
11
Dynamic Local Feature Analysis for Face Recognition
Johnny Ng,Humphrey Cheung +1 more
TL;DR: In this paper, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique, and a high recognition rate is achieved no matter the face is enrolled under different or bad lighting conditions.
11
•Dissertation
Contributions to facial feature extraction for face recognition
Huu-Tuan Nguyen
- 19 Sep 2014
TL;DR: A robust facial representation namely Local Patterns of Gradients (LPOG) is developed to capture meaningful features directly from gradient images and has low computational cost and is feasible to deploy in real life applications.
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