Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Ciprian Doru Giurcaneanu"'
Publikováno v:
Signal Processing. 155:170-181
The matching pursuit algorithm (MPA) is used in many applications for selecting the best predictors for a vector of measurements of size n from a dictionary that contains pn atoms, where usually n ≤ pn. A major unsolved problem is to determine the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f33c31e59d21eedba2c4e0f548bb39a9
https://doi.org/10.1016/j.sigpro.2018.09.033
https://doi.org/10.1016/j.sigpro.2018.09.033
Publikováno v:
EUSIPCO
In this work, we show how dictionary learning (DL) can be employed in the imputation of univariate and multivariate time series. In the multivariate case, we propose to use a structured dictionary. The size of the dictionary and the sparsity level ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f54e2ed3a9b460984e8a2215eaae0db
https://doi.org/10.23919/eusipco47968.2020.9287458
https://doi.org/10.23919/eusipco47968.2020.9287458
Autor:
Ciprian Doru Giurcaneanu, Lena Weissert, David E. Williams, Georgia Miskell, Jennifer Salmond, Maryam Alavi-Shoshtari
Publikováno v:
Environmental Modelling & Software. 101:34-50
Recent improvements in low-cost air quality instrumentation make deployment of dense networks of sensors possible. However, the shear volume of data from these networks means that traditional methods for data quality control and data analysis are no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13e2222f27b0ff77540c6764650b219f
https://doi.org/10.1016/j.envsoft.2017.12.002
https://doi.org/10.1016/j.envsoft.2017.12.002
Publikováno v:
EUSIPCO
In this work, we introduce an iterative method for the estimation of vector autoregressive (VAR) models with Granger and stability constraints. When the order of the model $(p)$ and the Granger sparsity pattern (GSP) are not known, the newly proposed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e654cf60c44d064f9f2c34b3dc85def3
https://doi.org/10.23919/eusipco.2019.8902516
https://doi.org/10.23919/eusipco.2019.8902516
Publikováno v:
Algorithms, Vol 12, Iss 9, p 178 (2019)
Algorithms
Volume 12
Issue 9
Algorithms
Volume 12
Issue 9
Finding the size of the dictionary is an open issue in dictionary learning (DL). We propose an algorithm that adapts the size during the learning process by using Information Theoretic Criteria (ITC) specialized to the DL problem. The algorithm is bu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e53ca833f1ed47e7b81956ff0d753194
https://www.mdpi.com/1999-4893/12/9/178
https://www.mdpi.com/1999-4893/12/9/178
Publikováno v:
EUSIPCO
We address the problem of selecting, from a given dictionary, a subset of predictors whose linear combination provides the best description for the vector of measurements. To this end, we apply the well-known matching pursuit algorithm (MPA). Even if
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c9ad423f095e363aa21b3e34d134e86
Publikováno v:
Entropy, Vol 20, Iss 1, p 76 (2018)
Entropy
Entropy; Volume 20; Issue 1; Pages: 76
Entropy
Entropy; Volume 20; Issue 1; Pages: 76
This work is focused on latent-variable graphical models for multivariate time series. We show how an algorithm which was originally used for finding zeros in the inverse of the covariance matrix can be generalized such that to identify the sparsity
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::875cdb037cd58ebf69cac0d3f270373b
http://www.mdpi.com/1099-4300/20/1/76
http://www.mdpi.com/1099-4300/20/1/76
Publikováno v:
EUSIPCO
Renormalized maximum likelihood (RNML) is a powerful concept from information theory. We show how it can be used to derive a criterion for selecting the order of vector autoregressive (VAR) processes. We prove that RNML criterion is strongly consiste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9e3e8c3847f33c5cf58f270b599dfce
https://doi.org/10.1109/eusipco.2016.7760228
https://doi.org/10.1109/eusipco.2016.7760228
Publikováno v:
IEEE Transactions on Signal Processing. 57:2445-2455
The newest approach to composite hypothesis testing proposed by Rissanen relies on the concept of optimally distinguishable distributions (ODD). The method is promising, but so far it has only been applied to a few simple examples. We derive the ODD
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::827efa9c0ee0a3792575faf64b60d720
https://doi.org/10.1109/tsp.2009.2017568
https://doi.org/10.1109/tsp.2009.2017568
Autor:
Ciprian Doru Giurcaneanu, Yinghua Yang, Petros Xanthopoulos, Vangelis Sakkalis, Michalis Zervakis, Vassilis Tsiaras, Eleni Karakonstantaki, Sifis Micheloyannis
Publikováno v:
IEEE Transactions on Information Technology in Biomedicine. 13:433-441
Summarization: Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46f93dbe6d8c95cbaf0e4dbe0cd0375f
https://doi.org/10.1109/titb.2008.923141
https://doi.org/10.1109/titb.2008.923141