Adaptive matching pursuit for sparse signal recovery
| Autor: | Vishal Monga, Hojjat Seyed Mousavi, Tiep H. Vu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Signal processing Mathematical optimization Optimization problem Computer science 020206 networking & telecommunications Machine Learning (stat.ML) 02 engineering and technology Bayesian inference Matching pursuit Machine Learning (cs.LG) Set (abstract data type) Computer Science - Learning Statistics - Machine Learning Prior probability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Integer programming Cholesky decomposition |
| Zdroj: | ICASSP |
| DOI: | 10.48550/arxiv.1610.08495 |
| Popis: | Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification. It is well-known that solving for the sparse coefficient vector to maximize these priors results in a hard non-convex and mixed integer programming problem. Most existing solutions to this optimization problem either involve simplifying assumptions/relaxations or are computationally expensive. We propose a new greedy and adaptive matching pursuit (AMP) algorithm to directly solve this hard problem. Essentially, in each step of the algorithm, the set of active elements would be updated by either adding or removing one index, whichever results in better improvement. In addition, the intermediate steps of the algorithm are calculated via an inexpensive Cholesky decomposition which makes the algorithm much faster. Results on simulated data sets as well as real-world image recovery challenges confirm the benefits of the proposed AMP, particularly in providing a superior cost-quality trade-off over existing alternatives. Comment: ICASSP |
| Databáze: | OpenAIRE |
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