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.
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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.
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Citations
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Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications
T. Warren Liao,Evangelos Triantaphyllou +1 more
- 01 Feb 2008
TL;DR: The main goal of the new field of data mining is the analysis of large and complex datasets, which may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a Manufacturing system, or data for business related decision-making.
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DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
Seunghun Lee,Sunghyun Cho,Sunghoon Im +2 more
- 20 Jun 2021
TL;DR: DRANet as mentioned in this paper disentangles image representations and transfers the visual attributes in a latent space for unsupervised cross-domain adaptation, which preserves the distinctiveness of each domain's characteristics.
Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification
TL;DR: A multiple feature-based superpixel-level decision fusion (MFSuDF) method is proposed for HSIs and LiDAR data classification that can achieve the overall accuracy of 73.64%, 93.88%, and 74.11% for Houston, Trento, and Missouri University and University of Florida Gulport data sets.
The use of kernel principal component analysis to model data distributions
TL;DR: The effectiveness of kernel principal component analysis (KPCA) is demonstrated by applying it to the higher-dimensional case of modelling an ensemble of images of handwritten digits, showing how it can be used to extract the digit information from noisy input images.
69
Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement
TL;DR: In this paper, a convolutional sparse filter (CSF) was proposed for weak impulsive signature enhancement and validated by both simulated data and experimental data, and the results demonstrate that CSF is an effective method for impulsive signatures enhancement that could be applied in rotating machines for incipient fault detection.
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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.
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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.
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