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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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.
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
- S. Bach, Alexander Binder, G. Montavon, F. Klauschen, K. Müller, W. Samek
- Computer SciencePloS one
- 10 July 2015
This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers by introducing a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks.
Kernel Principal Component Analysis
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.
An introduction to kernel-based learning algorithms
- K. Müller, S. Mika, Gunnar Rätsch, K. Tsuda, B. Schölkopf
- Computer ScienceIEEE Trans. Neural Networks
- 1 March 2001
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.…
Kernel PCA and De-Noising in Feature Spaces
- S. Mika, B. Schölkopf, Alex Smola, K. Müller, Matthias Scholz, Gunnar Rätsch
- Computer ScienceNIPS
- 1 December 1998
This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre- images in data reconstruction and de-noising on toy examples as well as on real world data.
Soft Margins for AdaBoost
It is found that ADABOOST asymptotically achieves a hard margin distribution, i.e. the algorithm concentrates its resources on a few hard-to-learn patterns that are interestingly very similar to Support Vectors.
Input space versus feature space in kernel-based methods
The geometry of feature space is reviewed, and the connection between feature space and input space is discussed by dealing with the question of how one can, given some vector in feature space, find a preimage in input space.
Single-trial analysis and classification of ERP components — A tutorial
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
- S. Bosse, D. Maniry, K. Müller, T. Wiegand, W. Samek
- Computer ScienceIEEE Transactions on Image Processing
- 6 December 2016
A deep neural network-based approach to image quality assessment (IQA) that allows for joint learning of local quality and local weights in an unified framework and shows a high ability to generalize between different databases, indicating a high robustness of the learned features.