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
Automated text mining process for corporate risk analysis and management
Ming-Fu Hsu,Chingho Chang,Jhih-Hong Zeng +2 more
TL;DR: The results show that the textual-based risk indicators are significantly and positively related to a corporate’s operation efficiency and echoes the recent trend of financial reporting regulations to add a new section on risk factors in annual reports.
10
Transient stability assessment of power systems using probabilistic neural network with enhanced feature selection and extraction
TL;DR: In this article, the authors presented transient stability assessment of a large actual 87-bus system and the IEEE 39-bus systems using the probabilistic neural network (PNN) with enhanced feature selection and extraction methods.
Big Data Analysis for Media Production
Josep Blat,Alun Evans,Hansung Kim,Evren Imre,Lukas Polok,Viorela Ila,Nikos Nikolaidis,Pavel Zemcik,Anastasios Tefas,Pavel Smrz,Adrian Hilton,Ioannis Pitas +11 more
- 01 Nov 2016
TL;DR: Improvements carried out in basic techniques for acceleration, clustering and visualization are discussed, which were necessary to deal with the very large multisource data, and can be applied to other big data problems in diverse application fields.
10
An automatic complex event processing rules generation system for the recognition of real-time IoT attack patterns
J. Roldán-Gómez,Juan Boubeta-Puig,Javier Carrillo-Mondejar,Juan Manuel Castelo Gómez,Jesus Martinez del Rincon +4 more
TL;DR: In this paper , the authors propose an architecture that is capable of generating complex event processing (CEP) rules automatically by integrating them with machine learning technologies, which is used to automatically detect attack patterns in real time, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies.
10
Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests
Nikhit Mago,Shikhar Srivastava,Rudresh D Shirwaikar,U. Dinesh Acharya,Leslie Lewis,M. Shivakumar +5 more
- 01 Jan 2016
TL;DR: This paper compares the usage of Support Vector Machines and Random Forests to predict Apnea of Prematurity at the end of the first week of the child's birth using data collected during the first three days of neonatal life and uses an optimization method called Synthesized Minority Oversampling Technique (SMOTE) to resolve the class imbalance problem observed in the data.
10
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