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
Breast cancer cell nuclei classification in histopathology images using deep neural networks
Yangqin Feng,Lei Zhang,Zhang Yi +2 more
TL;DR: An end-to-end DNN model for cell nuclei and non-nuclei classification of histopathology images is proposed and it is demonstrated that the proposed method can achieve promising performance incell nuclei classification.
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•Posted Content
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review.
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TL;DR: The theory and motivation of different common methods of feature selection and extraction and some of their applications are reviewed and some numerical implementations are shown for these methods.
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Low-rank preserving embedding
Yupei Zhang,Ming Xiang,Bo Yang +2 more
TL;DR: This paper proposes a new linear dimensionality reduction method by virtue of the lowest rank representation (LRR) of data, which is dubbed low-rank preserving embedding (LRPE), which achieves all data self-representations jointly and can thus extract the global structure of a data set as a whole.
78
Letter: On non-iterative learning algorithms with closed-form solution
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Prognostics and Health Management
Michael Pecht
- 01 Jan 2013
TL;DR: PHM-based-qualification combined with the PoF qualification process can enhance the evaluation of LED reliability in its actual life-cycle conditions to assess degradation, to detect early failures, to estimate the lifetime of LEDs, and to mitigate LED- based- product risks.
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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.
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