Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Philip Johan Havemann Jørgensen"'
Publikováno v:
MLSP
Bi-partite networks are commonly modelled using latent class or latent feature models. Whereas the existing latent class models admit marginalization of parameters specifying the strength of interaction between groups, existing latent feature models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16c6984d8d12094946d70aee99d97ad7
https://doi.org/10.1109/mlsp.2016.7738845
https://doi.org/10.1109/mlsp.2016.7738845
Autor:
Philip Johan Havemann Jørgensen, Nielsen, Søren F. V., Jesper Løve Hinrich, Mikkel N. Schmidt, Kristoffer Hougaard Madsen, Morten Mørup
Publikováno v:
Technical University of Denmark Orbit
Jørgensen, P J H, Nielsen, S F V, Hinrich, J L, Schmidt, M N, Madsen, K H & Mørup, M 2019, Analysis of Chromatographic Data using the Probabilistic PARAFAC2 . in Proceedings of Second Workshop on Machine Learning and the Physical Sciences . 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, 08/12/2019 .
Jørgensen, P J H, Nielsen, S F V, Hinrich, J L, Schmidt, M N, Madsen, K H & Mørup, M 2019, Analysis of Chromatographic Data using the Probabilistic PARAFAC2 . in Proceedings of Second Workshop on Machine Learning and the Physical Sciences . 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, 08/12/2019 .
PARAFAC2 is a widely applicable method often used for analyzing multi-way chromatographic data. We recently proposed a probabilistic framework for PARAFAC2[1]. The probabilistic formulations allow for a principled way of determining the number of lat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::301969dae0ca29f880cf3017470e1d9b
https://orbit.dtu.dk/en/publications/622f32f8-71d7-4953-98b0-996bf650d1e0
https://orbit.dtu.dk/en/publications/622f32f8-71d7-4953-98b0-996bf650d1e0
