Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery. Jiang, Q., de Lamare , R. C., Zakharov, Y., Li, S., & He, X. In *2018 26th European Signal Processing Conference (EUSIPCO)*, pages 1965-1969, Sep., 2018.

Paper doi abstract bibtex

Paper doi abstract bibtex

This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.

@InProceedings{8553389, author = {Q. Jiang and R. C. {de Lamare} and Y. Zakharov and S. Li and X. He}, booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)}, title = {Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery}, year = {2018}, pages = {1965-1969}, abstract = {This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.}, keywords = {approximation theory;compressed sensing;iterative methods;probability;RKA-NIHT;knowledge-aided normalized iterative hard thresholding algorithms;sparse recovery;compressive sensing applications;recursive KA-NIHT algorithm;Signal processing algorithms;Matching pursuit algorithms;Europe;Signal processing;Compressed sensing;Iterative algorithms;Simulation;compressed sensing;iterative hard thresholding;prior information;probability estimation;sparse recovery}, doi = {10.23919/EUSIPCO.2018.8553389}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437058.pdf}, }

Downloads: 0

{"_id":"tW7JKxPpi3RbzafpZ","bibbaseid":"jiang-delamare-zakharov-li-he-knowledgeaidednormalizediterativehardthresholdingalgorithmsforsparserecovery-2018","authorIDs":[],"author_short":["Jiang, Q.","de Lamare , R. C.","Zakharov, Y.","Li, S.","He, X."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Q."],"propositions":[],"lastnames":["Jiang"],"suffixes":[]},{"firstnames":["R.","C."],"propositions":["de Lamare"],"lastnames":[],"suffixes":[]},{"firstnames":["Y."],"propositions":[],"lastnames":["Zakharov"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Li"],"suffixes":[]},{"firstnames":["X."],"propositions":[],"lastnames":["He"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery","year":"2018","pages":"1965-1969","abstract":"This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.","keywords":"approximation theory;compressed sensing;iterative methods;probability;RKA-NIHT;knowledge-aided normalized iterative hard thresholding algorithms;sparse recovery;compressive sensing applications;recursive KA-NIHT algorithm;Signal processing algorithms;Matching pursuit algorithms;Europe;Signal processing;Compressed sensing;Iterative algorithms;Simulation;compressed sensing;iterative hard thresholding;prior information;probability estimation;sparse recovery","doi":"10.23919/EUSIPCO.2018.8553389","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437058.pdf","bibtex":"@InProceedings{8553389,\n author = {Q. Jiang and R. C. {de Lamare} and Y. Zakharov and S. Li and X. He},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery},\n year = {2018},\n pages = {1965-1969},\n abstract = {This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.},\n keywords = {approximation theory;compressed sensing;iterative methods;probability;RKA-NIHT;knowledge-aided normalized iterative hard thresholding algorithms;sparse recovery;compressive sensing applications;recursive KA-NIHT algorithm;Signal processing algorithms;Matching pursuit algorithms;Europe;Signal processing;Compressed sensing;Iterative algorithms;Simulation;compressed sensing;iterative hard thresholding;prior information;probability estimation;sparse recovery},\n doi = {10.23919/EUSIPCO.2018.8553389},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437058.pdf},\n}\n\n","author_short":["Jiang, Q.","de Lamare , R. C.","Zakharov, Y.","Li, S.","He, X."],"key":"8553389","id":"8553389","bibbaseid":"jiang-delamare-zakharov-li-he-knowledgeaidednormalizediterativehardthresholdingalgorithmsforsparserecovery-2018","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437058.pdf"},"keyword":["approximation theory;compressed sensing;iterative methods;probability;RKA-NIHT;knowledge-aided normalized iterative hard thresholding algorithms;sparse recovery;compressive sensing applications;recursive KA-NIHT algorithm;Signal processing algorithms;Matching pursuit algorithms;Europe;Signal processing;Compressed sensing;Iterative algorithms;Simulation;compressed sensing;iterative hard thresholding;prior information;probability estimation;sparse recovery"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2018url.bib","creationDate":"2021-02-13T15:38:40.411Z","downloads":0,"keywords":["approximation theory;compressed sensing;iterative methods;probability;rka-niht;knowledge-aided normalized iterative hard thresholding algorithms;sparse recovery;compressive sensing applications;recursive ka-niht algorithm;signal processing algorithms;matching pursuit algorithms;europe;signal processing;compressed sensing;iterative algorithms;simulation;compressed sensing;iterative hard thresholding;prior information;probability estimation;sparse recovery"],"search_terms":["knowledge","aided","normalized","iterative","hard","thresholding","algorithms","sparse","recovery","jiang","de lamare ","zakharov","li","he"],"title":"Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery","year":2018,"dataSources":["yiZioZximP7hphDpY","iuBeKSmaES2fHcEE9"]}