Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Shmueli, Yaniv"'
In recent years, distinctive-dictionary construction has gained importance due to his usefulness in data processing. Usually, one or more dictionaries are constructed from a training data and then they are used to classify signals that did not partic
Externí odkaz:
http://arxiv.org/abs/1502.04824
We present a fast randomized algorithm that computes a low rank LU decomposition. Our algorithm uses random projections type techniques to efficiently compute a low rank approximation of large matrices. The randomized LU algorithm can be parallelized
Externí odkaz:
http://arxiv.org/abs/1310.7202
We describe several algorithms for matrix completion and matrix approximation when only some of its entries are known. The approximation constraint can be any whose approximated solution is known for the full matrix. For low rank approximations, simi
Externí odkaz:
http://arxiv.org/abs/1302.6768
Publikováno v:
In Applied and Computational Harmonic Analysis March 2018 44(2):246-272
Akademický článek
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Publikováno v:
In Linear Algebra and Its Applications 15 September 2012 437(6):1356-1365
Autor:
Zeming Lin, Akin, Halil, Rao, Roshan, Hie, Brian, Zhongkai Zhu, Wenting Lu, Smetanin, Nikita, Verkuil, Robert, Kabeli, Ori, Shmueli, Yaniv, dos Santos Costa, Allan, Fazel-Zarandi, Maryam, Sercu, Tom, Candido, Salvatore, Rives, Alexander
Publikováno v:
Science; 3/17/2023, Vol. 379 Issue 6637, p1123-1130, 8p, 4 Color Photographs
Publication in the conference proceedings of SampTA, Bremen, Germany, 2013
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::789067919cec7004ebe1c4fcf4c8627f
We describe several algorithms for matrix completion and matrix approximation when only some of its entries are known. The approximation constraint can be any whose approximated solution is known for the full matrix. For low rank approximations, simi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::888a78d077eb5055893480083e62712b
http://arxiv.org/abs/1302.6768
http://arxiv.org/abs/1302.6768
The initial training phase of machine learning algorithms is usually computationally expensive as it involves the processing of huge matrices. Evolving datasets are challenging from this point of view because changing behavior requires updating the t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::176cabf73dad730526799cde99415807
http://urn.fi/URN:NBN:fi:jyu-201402051189
http://urn.fi/URN:NBN:fi:jyu-201402051189