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ICONIP
2007
14 years 11 months ago
Principal Component Analysis for Sparse High-Dimensional Data
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
Tapani Raiko, Alexander Ilin, Juha Karhunen
SIAMSC
2010
143views more  SIAMSC 2010»
14 years 8 months ago
Computing and Deflating Eigenvalues While Solving Multiple Right-Hand Side Linear Systems with an Application to Quantum Chromod
Abstract. We present a new algorithm that computes eigenvalues and eigenvectors of a Hermitian positive definite matrix while solving a linear system of equations with Conjugate G...
Andreas Stathopoulos, Konstantinos Orginos
ICML
2000
IEEE
15 years 10 months ago
Rates of Convergence for Variable Resolution Schemes in Optimal Control
This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
Andrew W. Moore, Rémi Munos
AMAI
2004
Springer
15 years 3 months ago
Approximate Probabilistic Constraints and Risk-Sensitive Optimization Criteria in Markov Decision Processes
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Dmitri A. Dolgov, Edmund H. Durfee
ML
2008
ACM
162views Machine Learning» more  ML 2008»
14 years 9 months ago
Incorporating prior knowledge in support vector regression
This paper explores the addition of constraints to the linear programming formulation of the support vector regression problem for the incorporation of prior knowledge. Equality an...
Fabien Lauer, Gérard Bloch