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AuthorYamac M.
AuthorAhishali M.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
Available date2022-04-26T12:31:19Z
Publication Date2021
Publication NameIEEE Transactions on Neural Networks and Learning Systems
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TNNLS.2021.3093818
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110918432&doi=10.1109%2fTNNLS.2021.3093818&partnerID=40&md5=d6304cda6b24601fbe2fe1b99bf4a42d
URIhttp://hdl.handle.net/10576/30598
AbstractSupport estimation (SE) of a sparse signal refers to finding the location indices of the nonzero elements in a sparse representation. Most of the traditional approaches dealing with SE problems are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery (SR) techniques to obtain support sets instead of directly mapping the nonzero locations from denser measurements (e.g., compressively sensed measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the convolutional sparse support estimator networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: 1) real-time and low-cost SE can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, and so on and 2) CSEN’s output can directly be used as “prior information,” which improves the performance of sparse SR algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBenchmarking
Compressed sensing
Convolution
Energy efficiency
Face recognition
Mapping
Signal reconstruction
Anomaly localizations
Compressive sensing
Iterative algorithm
Optimization techniques
Sparse representation
Sparse signal recoveries
State-of-the-art performance
Traditional approaches
Iterative methods
TitleConvolutional Sparse Support Estimator Network (CSEN): From Energy-Efficient Support Estimation to Learning-Aided Compressive Sensing
TypeArticle
dc.accessType Abstract Only


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