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
Advances in kernel methods: support vector learning
Bernhard Schölkopf,Christopher John Burges,Alexander J. Smola +2 more
- 08 Feb 1999
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
7.3K
Williamson, estimating the support of a high-dimensional distribution
Bernhard Schölkopf,John Platt,J Shawe Taylor +2 more
- 01 Jan 2001
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
5K
From frequency to meaning: vector space models of semantics
Peter D. Turney,Patrick Pantel +1 more
TL;DR: The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.
•Proceedings Article
Support Vector Method for Novelty Detection
Bernhard Schölkopf,Robert C. Williamson,Alexander J. Smola,John Shawe-Taylor,John Platt +4 more
- 29 Nov 1999
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data and is regularized by controlling the length of the weight vector in an associated feature space.
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
TL;DR: This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections by proposing a simple and efficient alternating minimization algorithm, dubbed iterative quantization (ITQ), and demonstrating an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.
2.1K
References
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Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
TL;DR: Rifamycin compounds having high antibacterial activity, consisting of powder colored from yellow to orange.
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•Proceedings Article
Extracting support data for a given task
Bernhard Schölkopf,Chris Burges,Vladimir Vapnik +2 more
- 20 Aug 1995
TL;DR: It is observed that three different types of handwritten digit classifiers construct their decision surface from strongly overlapping small subsets of the data base, which opens up the possibility of compressing data bases significantly by disposing of theData which is not important for the solution of a given task.
•Proceedings Article
Efficient Pattern Recognition Using a New Transformation Distance
Patrice Y. Simard,Patrice Y. Simard,Yann LeCun,John S. Denker,John S. Denker +4 more
- 30 Nov 1992
TL;DR: A new distance measure which can be made locally invariant to any set of transformations of the input and can be computed efficiently is proposed.
Incorporating Invariances in Support Vector Learning Machines
Bernhard Schölkopf,Bernhard Schölkopf,Chris Burges,Vladimir Vapnik +3 more
- 16 Jul 1996
TL;DR: This work presents a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.
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