A singular value decomposition updating algorithm for subspace tracking
SIAM Journal on Matrix Analysis and Applications
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
The nature of statistical learning theory
The nature of statistical learning theory
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
An Expectation-Maximization Approach to Nonlinear Component Analysis
Neural Computation
Sliding window adaptive SVD algorithms
IEEE Transactions on Signal Processing
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Efficient tracking of the dominant eigenspace of a normalized kernel matrix
Neural Computation
EURASIP Journal on Advances in Signal Processing
Adaptive kernel principal component analysis
Signal Processing
Computers and Industrial Engineering
A Fast Algorithm for Updating and Downsizing the Dominant Kernel Principal Components
SIAM Journal on Matrix Analysis and Applications
Artificial Intelligence Review
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The dominant set of eigenvectors of the symmetrical kernel Gram matrix is used in many important kernel methods (like e.g. kernel Principal Component Analysis, feature approximation, denoising, compression, prediction) in the machine learning area. Yet in the case of dynamic and/or large-scale data, the batch calculation nature and computational demands of the eigenvector decomposition limit these methods in numerous applications. In this paper we present an efficient incremental approach for fast calculation of the dominant kernel eigenbasis, which allows us to track the kernel eigenspace dynamically. Experiments show that our updating scheme delivers a numerically stable and accurate approximation for eigenvalues and eigenvectors at every iteration in comparison to the batch algorithm.