Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Joni Virta"'
Autor:
Reija Autio, Joni Virta, Klaus Nordhausen, Mikael Fogelholm, Maijaliisa Erkkola, Jaakko Nevalainen
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e44599 (2023)
BackgroundLoyalty card data automatically collected by retailers provide an excellent source for evaluating health-related purchase behavior of customers. The data comprise information on every grocery purchase, including expenditures on product grou
Externí odkaz:
https://doaj.org/article/83e6840a37724412aa011160ed5c94f0
Autor:
Jesse Pasanen, Tuija Leskinen, Kristin Suorsa, Anna Pulakka, Joni Virta, Kari Auranen, Sari Stenholm
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract We utilized compositional data analysis (CoDA) to study changes in the composition of the 24-h movement behaviors during an activity tracker based physical activity intervention. A total of 231 recently retired Finnish retirees were randomiz
Externí odkaz:
https://doaj.org/article/32b1b626780248998ebbe1560b2d7ee0
Publikováno v:
Journal of Statistical Software, Vol 98, Iss 1 (2021)
Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first reducing t
Externí odkaz:
https://doaj.org/article/3fae56349f204d4dae523d2280318db0
Publikováno v:
Austrian Journal of Statistics, Vol 49, Iss 4 (2020)
We consider complex valued linear blind source separation, where the signal dimension might be smaller than the dimension of the observable data vector. In order to measure the success of the signal separation, we propose an extension of the minimum
Externí odkaz:
https://doaj.org/article/74d02096682c4ba1a5de49c9eeda42a1
Publikováno v:
Genome Biology, Vol 19, Iss 1, Pp 1-18 (2018)
Abstract There is an increased need for integrative analyses of multi-omic data. We present and benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. We find that tICA outperforms compet
Externí odkaz:
https://doaj.org/article/09fa155d91294536b1cb6f1dfd037a7e
Autor:
Reija Autio, Joni Virta, Klaus Nordhausen, Mikael Fogelholm, Maijaliisa Erkkola, Jaakko Nevalainen
BACKGROUND Loyalty card data automatically collected by retailers provide an excellent source for evaluating health-related purchase behaviour of customers. The data comprise information on every grocery purchase, including expenditures on products a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58bbcfd052930c0b8cab5260c11fdce8
https://doi.org/10.2196/preprints.44599
https://doi.org/10.2196/preprints.44599
Publikováno v:
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME).
Many modern multivariate time series datasets contain a large amount of noise, and the first step of the data analysis is to separate the noise channels from the signals of interest. A crucial part of this dimension reduction is determining the numbe
Autor:
Joni Virta, Andreas Artemiou
Publikováno v:
Pattern Recognition. 138:109401
We develop a dimension reduction framework for data consisting of matrices of counts. Our model is based on assuming the existence of a small amount of independent normal latent variables that drive the dependency structure of the observed data, and
Autor:
Aurore Archimbaud, Feriel Boulfani, Xavier Gendre, Klaus Nordhausen, Anne Ruiz-Gazen, Joni Virta
Publikováno v:
Econometrics and Statistics. Elsevier
Multivariate functional anomaly detection has received a large amount of attention recently. Accounting both the time dimension and the correlations between variables is challenging due to the existence of different types of outliers and the dimensio
In this article, we propose a general nonlinear sufficient dimension reduction (SDR) framework when both the predictor and response lie in some general metric spaces. We construct reproducing kernel Hilbert spaces whose kernels are fully determined b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c174fd043df131105f32306007b9b88