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» On Constrained Sparse Matrix Factorization
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FOCS
2008
IEEE
15 years 4 months ago
Near-Optimal Sparse Recovery in the L1 Norm
Abstract— We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a highdimensional vector x ∈ Rn from its lower-dimensional sketch Ax...
Piotr Indyk, Milan Ruzic
IJCAI
2007
14 years 11 months ago
Fast Incremental Square Root Information Smoothing
We propose a novel approach to the problem of simultaneous localization and mapping (SLAM) based on incremental smoothing, that is suitable for real-time applications in large-sca...
Michael Kaess, Ananth Ranganathan, Frank Dellaert
JMLR
2008
188views more  JMLR 2008»
14 years 9 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
CORR
2012
Springer
225views Education» more  CORR 2012»
13 years 5 months ago
Compressive Principal Component Pursuit
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
John Wright, Arvind Ganesh, Kerui Min, Yi Ma
COMPUTING
2007
103views more  COMPUTING 2007»
14 years 9 months ago
An algebraic approach for H-matrix preconditioners
Hierarchical matrices (H-matrices) approximate matrices in a data-sparse way, and the approximate arithmetic for H-matrices is almost optimal. In this paper we present an algebrai...
S. Oliveira, F. Yang