References

[APP+18]

Ardavan Afshar, Ioakeim Perros, Evangelos E Papalexakis, Elizabeth Searles, Joyce Ho, and Jimeng Sun. COPA: constrained PARAFAC2 for sparse & large datasets. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 793–802. 2018.

[ASB+08]

José Manuel Amigo, Thomas Skov, Rasmus Bro, Jordi Coello, and Santiago Maspoch. Solving GC-MS problems with PARAFAC2. TrAC Trends in Analytical Chemistry, 27(8):714–725, 2008.

[BPC11]

Stephen Boyd, Neal Parikh, and Eric Chu. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc, 2011.

[CBKA07]

Peter A Chew, Brett W Bader, Tamara G Kolda, and Ahmed Abdelali. Cross-language information retrieval using PARAFAC2. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 143–152. 2007.

[CB18]

Jeremy E Cohen and Rasmus Bro. Nonnegative PARAFAC2: a flexible coupling approach. In International Conference on Latent Variable Analysis and Signal Separation, 89–98. Springer, 2018.

[Con13]

Laurent Condat. A direct algorithm for 1-d total variation denoising. IEEE Signal Processing Letters, 20(11):1054–1057, 2013.

[FHHoflingT07]

Jerome Friedman, Trevor Hastie, Holger Höfling, and Robert Tibshirani. Pathwise coordinate optimization. The annals of applied statistics, 1(2):302–332, 2007.

[GTP20]

Ekta Gujral, Georgios Theocharous, and Evangelos E Papalexakis. SPADE: streaming PARAFAC2 DEcomposition for large datasets. In Proceedings of the 2020 SIAM International Conference on Data Mining, 577–585. SIAM, 2020.

[Har72]

Richard A Harshman. PARAFAC2: mathematical and technical notes. UCLA Working Papers in Phonetics, 22:30–44, 1972.

[HL96]

Richard A Harshman and Margaret E Lundy. Uniqueness proof for a family of models sharing features of tucker's three-mode factor analysis and parafac/candecomp. Psychometrika, 61(1):133–154, 1996.

[HSL16]

Kejun Huang, Nicholas D Sidiropoulos, and Athanasios P Liavas. A flexible and efficient algorithmic framework for constrained matrix and tensor factorization. IEEE Transactions on Signal Processing, 64(19):5052–5065, 2016.

[KTBB99]

Henk AL Kiers, Jos MF Ten Berge, and Rasmus Bro. PARAFAC2—Part I. a direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics: A Journal of the Chemometrics Society, 13(3-4):275–294, 1999.

[KPAP19]

Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, and Maja Pantic. Tensorly: tensor learning in python. Journal of Machine Learning Research, 20(26):1–6, 2019.

[MCMorup17]

Kristoffer H Madsen, Nathan W Churchill, and Morten Mørup. Quantifying functional connectivity in multi-subject fMRI data using component models. Human brain mapping, 38(2):882–899, 2017.

[RLXH20]

Yifei Ren, Jian Lou, Li Xiong, and Joyce C Ho. Robust irregular tensor factorization and completion for temporal health data analysis. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 1295–1304. 2020.

[RBJ+20]

Marie Roald, Suchita Bhinge, Chunying Jia, Vince Calhoun, Tülay Adalı, and Evrim Acar. Tracing network evolution using the PARAFAC2 model. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1100–1104. IEEE, 2020.

[RSC+22]

Marie Roald, Carla Schenker, Vince D Calhoun, Tülai Adali, Rasmus Bro, Jeremy E Cohen, and Evrim Acar. An ao-admm approach to constraining PARAFAC2 on all modes. Accepted for publication in SIAM Journal on Mathematics of Data Science, 2022. URL: https://arxiv.org/abs/2110.01278.

[RSCA21]

Marie Roald, Carla Schenker, Jeremy E Cohen, and Evrim Acar. PARAFAC2 AO-ADMM: constraints in all modes. In Proceedings of the 29th European Signal Processing Conference. EURASIP, 2021.

[RB13]

C Ruckebusch and L Blanchet. Multivariate curve resolution: a review of advanced and tailored applications and challenges. Analytica chimica acta, 765:28–36, 2013.

[ROF92]

Leonid I Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259–268, 1992.

[Sto08]

Quentin F Stout. Unimodal regression via prefix isotonic regression. Computational Statistics & Data Analysis, 53(2):289–297, 2008.

[VBKGD20]

Mark H Van Benthem, Timothy J Keller, Gregory D Gillispie, and Stephanie A DeJong. Getting to the core of PARAFAC2, a nonnegative approach. Chemometrics and Intelligent Laboratory Systems, 206:104127, 2020.

[WGB+99]

Barry M Wise, Neal B Gallagher, Stephanie Watts Butler, Daniel D White Jr, and Gabriel G Barna. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics: A Journal of the Chemometrics Society, 13(3-4):379–396, 1999.

[WGM01]

Barry M Wise, Neal B Gallagher, and Elaine B Martin. Application of PARAFAC2 to fault detection and diagnosis in semiconductor etch. Journal of Chemometrics: A Journal of the Chemometrics Society, 15(4):285–298, 2001.

[YB21]

Huiwen Yu and Rasmus Bro. PARAFAC2 and local minima. Chemometrics and Intelligent Laboratory Systems, pages 104446, 2021.