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IGARSS
2009
15 years 1 months ago
Unmixing Sparse Hyperspectral Mixtures
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or ...
Marian-Daniel Iordache, José M. Bioucas-Dia...
134
Voted
CORR
2011
Springer
191views Education» more  CORR 2011»
14 years 10 months ago
A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels
We propose a factor-graph-based approach to joint channel-estimationand-decoding of bit-interleaved coded orthogonal frequency division multiplexing (BICM-OFDM). In contrast to ex...
Philip Schniter
125
Voted
FOCM
2011
175views more  FOCM 2011»
14 years 10 months ago
Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization
The matrix rank minimization problem has applications in many fields such as system identification, optimal control, low-dimensional embedding etc. As this problem is NP-hard in ...
Donald Goldfarb, Shiqian Ma
164
Voted
MP
2011
14 years 10 months ago
Null space conditions and thresholds for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
214
Voted
SIAMIS
2011
14 years 10 months ago
Gradient-Based Methods for Sparse Recovery
The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, ...
William W. Hager, Dzung T. Phan, Hongchao Zhang