Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
The nature of statistical learning theory
The nature of statistical learning theory
Matrix computations (3rd ed.)
An equivalence between sparse approximation and support vector machines
Neural Computation
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Sparse on-line Gaussian processes
Neural Computation
Some Greedy Algorithms for Sparse Nonlinear Regression
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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Gaussian processes have been widely applied to regression problems with good performance. However, they can be computationally expensive. In order to reduce the computational cost, there have been recent studies on using sparse approximations in gaussian processes. In this article, we investigate properties of certain sparse regression algorithms that approximately solve a gaussian process. We obtain approximation bounds and compare our results with related methods.