Analysis of Chromatographic Data using the Probabilistic PARAFAC2

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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 latent components as well as modeling heteroscedastic noise. In this work we present a summary of the probabilistic PARAFAC2 models and their properties by revisiting the previous results of the analyzed data sets in a concise fashion.
Original languageEnglish
Title of host publicationProceedings of Second Workshop on Machine Learning and the Physical Sciences
Number of pages5
Publication date2019
Publication statusPublished - 2019
Event33rd Conference on Neural Information Processing Systems - Vancouver Convention Centre, Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019
Conference number: 33
https://nips.cc/Conferences/2019/

Conference

Conference33rd Conference on Neural Information Processing Systems
Number33
LocationVancouver Convention Centre
CountryCanada
CityVancouver
Period08/12/201914/12/2019
Internet address

Cite this

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
@inproceedings{622f32f871d7495398b0996bf650d1e0,
title = "Analysis of Chromatographic Data using the Probabilistic PARAFAC2",
abstract = "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 latent components as well as modeling heteroscedastic noise. In this work we present a summary of the probabilistic PARAFAC2 models and their properties by revisiting the previous results of the analyzed data sets in a concise fashion.",
author = "J{\o}rgensen, {Philip Johan Havemann} and Nielsen, {S{\o}ren F. V.} and Hinrich, {Jesper L{\o}ve} and Schmidt, {Mikkel N{\o}rgaard} and Madsen, {Kristoffer Hougaard} and Morten M{\o}rup",
year = "2019",
language = "English",
booktitle = "Proceedings of Second Workshop on Machine Learning and the Physical Sciences",

}

Jørgensen, PJH, Nielsen, SFV, Hinrich, JL, Schmidt, MN, Madsen, KH & 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.

Analysis of Chromatographic Data using the Probabilistic PARAFAC2. / Jørgensen, Philip Johan Havemann; Nielsen, Søren F. V. ; Hinrich, Jesper Løve; Schmidt, Mikkel Nørgaard; Madsen, Kristoffer Hougaard; Mørup, Morten.

Proceedings of Second Workshop on Machine Learning and the Physical Sciences. 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Analysis of Chromatographic Data using the Probabilistic PARAFAC2

AU - Jørgensen, Philip Johan Havemann

AU - Nielsen, Søren F. V.

AU - Hinrich, Jesper Løve

AU - Schmidt, Mikkel Nørgaard

AU - Madsen, Kristoffer Hougaard

AU - Mørup, Morten

PY - 2019

Y1 - 2019

N2 - 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 latent components as well as modeling heteroscedastic noise. In this work we present a summary of the probabilistic PARAFAC2 models and their properties by revisiting the previous results of the analyzed data sets in a concise fashion.

AB - 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 latent components as well as modeling heteroscedastic noise. In this work we present a summary of the probabilistic PARAFAC2 models and their properties by revisiting the previous results of the analyzed data sets in a concise fashion.

M3 - Article in proceedings

BT - Proceedings of Second Workshop on Machine Learning and the Physical Sciences

ER -

Jørgensen PJH, Nielsen SFV, Hinrich JL, Schmidt MN, Madsen KH, Mørup M. Analysis of Chromatographic Data using the Probabilistic PARAFAC2. In Proceedings of Second Workshop on Machine Learning and the Physical Sciences. 2019