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
ECG biometric authentication based on non-fiducial approach using kernel methods
Maryamsadat Hejazi,Syed Abdul Rahman Al-Haddad,Yashwant Prasad Singh,Shaiful Jahari Hashim,Ahmad Fazli Abdul Aziz +4 more
TL;DR: A new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT).
104
Kernel principal component analysis and support vector machines for stock price prediction
Huseyin Ince,Theodore B. Trafalis +1 more
- 25 Jul 2004
TL;DR: This work assumes that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship, and comparison shows that SVR and MLP networks require different inputs.
102
Distinguishing wild from cultivated agarwood (Aquilaria spp.) using direct analysis in real time and time of-flight mass spectrometry
Edgard O. Espinoza,Cady A. Lancaster,Natasha M. Kreitals,Masataka Hata,Robert B. Cody,Robert A. Blanchette +5 more
TL;DR: The direct analysis of wood for the diagnostic chromones using DART-TOFMS followed by discriminant analysis is sufficiently robust to differentiate wild from cultivated agarwood and provides strong inference for the origin of the agarwoods.
A Neural Network-Based On-Device Learning Anomaly Detector for Edge Devices
TL;DR: Experiments show that ONLAD has favorable anomaly detection capability in an environment that simulates concept drift, andONLAD Core realizes on-device learning for edge devices at low power consumption, which realizes standalone execution where data transfers between edge and server are not required.
Compressive Hyperspectral Imaging via Sparse Tensor and Nonlinear Compressed Sensing
TL;DR: A new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors, and a new tensor-NCS (T- NCS) algorithm for noniterative recovery of hyperspectral images is developed.
99
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