publication . Preprint . 2018

Probabilistic PARAFAC2

Jørgensen, Philip J. H.; Nielsen, Søren F. V.; Hinrich, Jesper L.; Schmidt, Mikkel N.; Madsen, Kristoffer H.; Mørup, Morten;
Open Access English
  • Published: 21 Jun 2018
Abstract
Comment: 16 pages (incl. 4 pages of supplemental material), 5 figures
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Learning
Download from
55 references, page 1 of 4

[4] C. J. Appellof and E. R. Davidson, “Strategies for analyzing data from video fluorometric monitoring of liquid chromatographic effluents,” Analytical chemistry, vol. 53, no. 13, pp. 2053-2056, 1981.

[5] T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM review, vol. 51, no. 3, pp. 455-500, 2009.

[6] M. Mørup, “Applications of tensor (multiway array) factorizations and decompositions in data mining,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 24-40, 2011. [OpenAIRE]

[7] L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika, vol. 31, no. 3, pp. 279-311, 1966.

[8] F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Studies in Applied Mathematics, vol. 6, no. 1-4, pp. 164-189, 1927. [OpenAIRE]

[9] R. Bro, C. A. Andersson, and H. A. Kiers, “Parafac2-part ii. modeling chromatographic data with retention time shifts,” Journal of Chemometrics, vol. 13, no. 3-4, pp. 295-309, 1999.

[10] R. A. Harshman and M. E. Lundy, “Uniqueness proof for a family of models sharing features of tucker's three-mode factor analysis and parafac/candecomp,” Psychometrika, vol. 61, no. 1, pp. 133- 154, 1996. [OpenAIRE]

[11] J. M. ten Berge and H. A. Kiers, “Some uniqueness results for parafac2,” Psychometrika, vol. 61, no. 1, pp. 123-132, 1996.

[12] H. A. Kiers, J. M. Ten Berge, and R. Bro, “Parafac2-part i. a direct fitting algorithm for the parafac2 model,” Journal of Chemometrics, vol. 13, no. 3-4, pp. 275-294, 1999.

[13] B. M. Wise, N. B. Gallagher, and E. B. Martin, “Application of parafac2 to fault detection and diagnosis in semiconductor etch,” Journal of chemometrics, vol. 15, no. 4, pp. 285-298, 2001.

[14] M. Weis, D. Jannek, F. Roemer, T. Guenther, M. Haardt, and P. Husar, “Multi-dimensional parafac2 component analysis of multi-channel eeg data including temporal tracking,” in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. IEEE, 2010, pp. 5375-5378. [OpenAIRE]

[15] K. H. Madsen, N. W. Churchill, and M. Mørup, “Quantifying functional connectivity in multi-subject fmri data using component models,” Human Brain Mapping, 2016.

[16] P. A. Chew, B. W. Bader, T. G. Kolda, and A. Abdelali, “Crosslanguage information retrieval using parafac2,” in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007, pp. 143-152.

[17] Y. Panagakis and C. Kotropoulos, “Automatic music tagging via parafac2,” in acoustics, speech and signal processing (ICASSP), 2011 IEEE international conference on. IEEE, 2011, pp. 481-484.

[1] J. D. Carroll and J. J. Chang, “Analysis of individual differences in multidimensional scaling via an n-way generalization of ”EckartYoung” decomposition,” Psychometrika, vol. 35, no. 3, pp. 283-319, [18] E. Pantraki and C. Kotropoulos, “Automatic image tagging and 1970. recommendation via parafac2,” in Machine Learning for Signal

55 references, page 1 of 4
Abstract
Comment: 16 pages (incl. 4 pages of supplemental material), 5 figures
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Learning
Download from
55 references, page 1 of 4

[4] C. J. Appellof and E. R. Davidson, “Strategies for analyzing data from video fluorometric monitoring of liquid chromatographic effluents,” Analytical chemistry, vol. 53, no. 13, pp. 2053-2056, 1981.

[5] T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM review, vol. 51, no. 3, pp. 455-500, 2009.

[6] M. Mørup, “Applications of tensor (multiway array) factorizations and decompositions in data mining,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 24-40, 2011. [OpenAIRE]

[7] L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika, vol. 31, no. 3, pp. 279-311, 1966.

[8] F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Studies in Applied Mathematics, vol. 6, no. 1-4, pp. 164-189, 1927. [OpenAIRE]

[9] R. Bro, C. A. Andersson, and H. A. Kiers, “Parafac2-part ii. modeling chromatographic data with retention time shifts,” Journal of Chemometrics, vol. 13, no. 3-4, pp. 295-309, 1999.

[10] R. A. Harshman and M. E. Lundy, “Uniqueness proof for a family of models sharing features of tucker's three-mode factor analysis and parafac/candecomp,” Psychometrika, vol. 61, no. 1, pp. 133- 154, 1996. [OpenAIRE]

[11] J. M. ten Berge and H. A. Kiers, “Some uniqueness results for parafac2,” Psychometrika, vol. 61, no. 1, pp. 123-132, 1996.

[12] H. A. Kiers, J. M. Ten Berge, and R. Bro, “Parafac2-part i. a direct fitting algorithm for the parafac2 model,” Journal of Chemometrics, vol. 13, no. 3-4, pp. 275-294, 1999.

[13] B. M. Wise, N. B. Gallagher, and E. B. Martin, “Application of parafac2 to fault detection and diagnosis in semiconductor etch,” Journal of chemometrics, vol. 15, no. 4, pp. 285-298, 2001.

[14] M. Weis, D. Jannek, F. Roemer, T. Guenther, M. Haardt, and P. Husar, “Multi-dimensional parafac2 component analysis of multi-channel eeg data including temporal tracking,” in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. IEEE, 2010, pp. 5375-5378. [OpenAIRE]

[15] K. H. Madsen, N. W. Churchill, and M. Mørup, “Quantifying functional connectivity in multi-subject fmri data using component models,” Human Brain Mapping, 2016.

[16] P. A. Chew, B. W. Bader, T. G. Kolda, and A. Abdelali, “Crosslanguage information retrieval using parafac2,” in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007, pp. 143-152.

[17] Y. Panagakis and C. Kotropoulos, “Automatic music tagging via parafac2,” in acoustics, speech and signal processing (ICASSP), 2011 IEEE international conference on. IEEE, 2011, pp. 481-484.

[1] J. D. Carroll and J. J. Chang, “Analysis of individual differences in multidimensional scaling via an n-way generalization of ”EckartYoung” decomposition,” Psychometrika, vol. 35, no. 3, pp. 283-319, [18] E. Pantraki and C. Kotropoulos, “Automatic image tagging and 1970. recommendation via parafac2,” in Machine Learning for Signal

55 references, page 1 of 4
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