What is the goal of sensory coding?
Neural Computation
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Probability Models for Clutter in Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
AI Game Programming Wisdom
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Digital Image Processing
Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
A Compression Approach to Support Vector Model Selection
The Journal of Machine Learning Research
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Global convergence of Oja's subspace algorithm for principal component extraction
IEEE Transactions on Neural Networks
The pre-image problem in kernel methods
IEEE Transactions on Neural Networks
Step Size Adaptation in Reproducing Kernel Hilbert Space
The Journal of Machine Learning Research
A two-step neural-network based algorithm for fast image super-resolution
Image and Vision Computing
A two stage algorithm for face recognition: 2DPCA and within-class scatter minimization
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
KPCA denoising and the pre-image problem revisited
Digital Signal Processing
Example-Based Learning for Single-Image Super-Resolution
Proceedings of the 30th DAGM symposium on Pattern Recognition
POCS-Based Texture Reconstruction Method Using Clustering Scheme by Kernel PCA
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
POCS-Based Annotation Method Using Kernel PCA for Semantic Image Retrieval
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Nonlinear Component Analysis for Large-Scale Data Set Using Fixed-Point Algorithm
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Limited stochastic meta-descent for kernel-based online learning
Neural Computation
Realistic Depth Blur for Images with Range Data
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Novel multiclass classifiers based on the minimization of the within-class variance
IEEE Transactions on Neural Networks
A principal component regression strategy for estimating motion
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
Error concealment by means of clustered blockwise PCA
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Matrix-based kernel principal component analysis for large-scale data set
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A two stage algorithm for face recognition: 2DPCA and within-class scatter minimization
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Adaptive kernel principal component analysis
Signal Processing
Sparse Kernel PCA by Kernel K-means and preimage reconstruction algorithms
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
ICNC'09 Proceedings of the 5th international conference on Natural computation
Penalized preimage learning in kernel principal component analysis
IEEE Transactions on Neural Networks
Adaptive kernel-based image denoising employing semi-parametric regularization
IEEE Transactions on Image Processing
Fusion of range and color images for denoising and resolution enhancement with a non-local filter
Computer Vision and Image Understanding
Fast covariance computation and dimensionality reduction for sub-window features in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
The complex Gaussian kernel LMS algorithm
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Fast principal component analysis based on hardware architecture of generalized Hebbian algorithm
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Efficient GHA-based hardware architecture for texture classification
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Missing texture reconstruction method based on perceptually optimized algorithm
EURASIP Journal on Advances in Signal Processing
Finding pre-images via evolution strategies
Applied Soft Computing
Centered subset kernel PCA for denoising
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Regularized Pre-image Estimation for Kernel PCA De-noising
Journal of Signal Processing Systems
Linear and kernel methods for multivariate change detection
Computers & Geosciences
Kernel principal component analysis for large scale data set
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
An improved kernel principal component analysis for large-scale data set
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Linear replicator in kernel space
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Classification of tuberculosis digital images using hybrid evolutionary extreme learning machines
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Kernel-based principal components analysis on large telecommunication data
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Evolving artificial neural networks for nonlinear feature construction
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A unified SVM framework for signal estimation
Digital Signal Processing
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In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution and denoising performance are comparable to existing methods.