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ICA
2007
Springer
16 years 26 days ago
Sparse Component Analysis in Presence of Noise Using an Iterative EM-MAP Algorithm
Abstract. In this paper, a new algorithm for source recovery in underdetermined Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented i...
Hadi Zayyani, Massoud Babaie-Zadeh, G. Hosein Mohi...
ICML
2006
IEEE
16 years 21 days ago
Automatic basis function construction for approximate dynamic programming and reinforcement learning
We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results ...
Philipp W. Keller, Shie Mannor, Doina Precup
ICIAP
2005
ACM
16 years 7 days ago
Robust Particle Filtering for Object Tracking
Abstract. This paper addresses the filtering problem when no assumption about linearity or gaussianity is made on the involved density functions. This approach, widely known as pa...
Daniel Rowe, Ignasi Rius, Jordi Gonzàlez, J...
NIPS
2008
15 years 8 months ago
Sparse Signal Recovery Using Markov Random Fields
Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the th...
Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Ric...
NIPS
2004
15 years 8 months ago
A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees
We introduce a new algorithm based on linear programming that approximates the differential value function of an average-cost Markov decision process via a linear combination of p...
Daniela Pucci de Farias, Benjamin Van Roy