Abstract
A survey of our recent work on probabilistic NMF is provided. All variants discussed here are illustrated by their application to the analysis of failure patterns emerging from manufacturing and processing silicon wafers. It starts with binNMF, a variant developed to apply NMF to binary data sets. The latter are modeled as a probabilistic superposition of a finite number of intrinsic continuous-valued failure patterns characteristic for the manufacturing process. We further discuss related theoretical work on a semi-non-negative matrix factorization based on the logistic function, which we called logistic NMF. While addressing uniqueness issues, we propose a Bayesian Optimality Criterion for NMF and a determinant criterion to geometrically constrain the solutions of NMF problems, leading to detNMF. This approach also provides an intuitive explanation for the often used multilayer approach. Finally, we present a Variational Bayes NMF (VBNMF) algorithm which represents a generalization of the famous Lee–Seung method. We also demonstrate its ability to estimate the intrinsic dimension (model order) of the NMF method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
R. Schachtner, G. Pöppel, E.W. Lang, IEEE Trans. Circuits Syst. I 57(7), 1439 (2010)
A.M. Tomé, R. Schachtner, V. Vigneron, C.G. Puntonet, E.W. Lang, Multidimensional Systems and Signal Processing (2013), pp. 1–19. doi:10.1007/s11045-013-0240-9
R. Schachtner, G. Pöppel, E.W. Lang, Digit. Signal Process. 21(4), 528 (2011)
R. Schachtner, G. Pöppel, A. Tomé, C. Puntonet, E.W. Lang, Neurocomputing 138, 142 (2014)
A. Cichocki, arXiv:1403.2048v4 [cs.ET] 24 Aug 2014 (2014), pp. 1–30
P. Paatero, U. Tapper, Environmetrics 5(2), 111 (1994)
P. Paatero, Chemom. Intell. Lab. Syst. 37, 23 (1997)
D.D. Lee, H.S. Seung, Nature 401(6755), 788 (1999)
A. Cichocki, S. Amari, R. Zdunek, A.H. Phan, Non-negative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation (Wiley-Blackwell, Oxford, 2009)
R. Schachtner, G. Pöppel, E.W. Lang, in \(32{nd}\) Annual Meeting of the German Classification Society (GfKl) (2008), pp. 755–764
R. Schachtner, G. Pöppel, A.M. Tomé, E.W. Lang, in Proceedings of 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, Paraty, Brazil, 15–18 March 2009, pp. 106–113. doi:10.1007/978-3-642-00599-2_14
R. Schachtner, G. Pöppel, E.W. Lang, in Proceedings of the 2nd International Workshop on Cognitive Information Processing on Elba Island, CIP2010 (2010), pp. 57–62
R. Schachtner, G. Pöppel, A.M. Tomé, E.W. Lang, Pattern Recognit. Lett. 45, 251 (2014). doi:10.1016/j.patrec.2014.04.013
A. Cichocki, R. Zdunek, Advances in Neural Networks (ISNN 2007). Lecture Notes in Computer Science, vol. 4493 (Springer, Berlin, 2007), pp. 793–802
D.D. Lee, H.S. Seung, NIPS 13, 556–562 (2001)
A.I. Schein, L.K. Saul, L.H. Ungar, in Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics (2003), pp. 1–8
S. Lee, J.Z. Huang, J. Hu, Ann. Appl. Stat. 4(3), 1579 (2010)
E. Meeds, Z. Ghahramani, R.M. Neal, S.T. Roweis, Bernoulli 19(8), 977 (2007)
E. Lang, R. Schachtner, D. Lutter, D. Herold, A. Kodewitz, F. Blöchl, F.J. Theis, I.R. Keck, J.G. Saez, P.G. Vilda, A.M. Tomé, New advances in biomedical signal processing, in Exploratory Matrix Factorization Techniques for Large Scale Biomedical Data Sets, ed. by J.M. Górriz-Sáez, E.W. Lang, J. Ramírez (Bentham Science Publishers, 2011), pp. 26–47. doi:10.2174/97816080521891110101
A. Cichocki, R. Zdunek, S. Amari, in 2006 IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP 2006, Toulouse, France, 14–19 May 2006 (2006), pp. 621–624. doi:10.1109/ICASSP.2006.1661352
M.E. Tipping, in Proceedings of Advances in Neural Information Processing Systems II (NIPS 1998) (1999), pp. 592–598
A. Kabán, E. Bingham, T. Hirsimäki, in Proceedings of 4th SIAM International Conference on Data Mining (2004), pp. 462–466
M. Collins, S. Dasgupta, R.E. Schapire, in NIPS 11 (2001), pp. 592–598
E. Bingham, A. Kaban, M. Fortelius, Pattern Anal. Appl. 12(1), 55 (2009)
K.H. Knuth, in Proceedings of the 13th European Signal Processing Conference (EUSIPCO 2005) (2005), pp. 1–8. arXiv:1311.3001 [stat.ML]
D.J.C. MacKay, Information Theory, Inference, and Learning Algorithm (Cambridge University Press, Cambridge, 2003). http://www.inference.phy.cam.ac.uk/mackay/itila/
A. Cichocki, R. Zdunek, S. Amari, Csiszar’s Divergences for Non-Negative Matrix Factorization, Family of New Algorithms (Springer, Berlin, 2006), pp. 32–39
I. Dhillon, S. Sra, in Proceedings of Neural Information Processing Systems (NIPS) (2005)
S. Sra, I.S. Dhillon, Nonnegative matrix approximation: algorithms and applications. Technical report, Computer Sciences, University of Texas, Technical Report, # TR-06-27 (2006)
A. Cichocki, R. Zdunek, S. Amari, IEEE Signal Process. Mag. 142, 142 (2008)
P.K. Hopke, in EPA Workshop Proceedings, Materials from the Work shop on UNMIX and PMF as Applied to PM2.5. (2000). http://www.epa.gov/ttnamti1/files/ambient/pm25/workshop/laymen.pdf
P. Sajda, S. Du, T. Brown, L. Parra, R. Stoyanova, in 4th International Symposium on Independent Component Analysis and Blind Signal Separation (2003), pp. 71–76
P. Sajda, S. Du, T.R. Brown, R. Stoyanova, D.C. Shungu, X. Mao, L.C. Parra, IEEE Trans. Med. Imaging 23(12), 1453 (2004)
L. Miao, H. Qi, IEEE Trans. Geosci. Remote Sens. 45(3), 765 (2007)
M.D. Craig, IEEE Trans. Geosci. Remote Sens. 32(3), 542 (1994)
C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1996)
P. Hoyer, in Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (2002), pp. 557–565
M.N. Schmidt, H. Laurberg, Comput. Intell. Neurosci. 1 (2008). doi:10.1155/2008/361705
A.T. Cemgil, Comput. Intell. Neurosci. 1 (2009). doi:10.1155/2009/785152
C. Févotte, A.T. Cemgil, in Proceedings of 17th European Signal Processing Conference (EUSIPCO’09) (2009), pp. 1–5
S. Moussaoui, D. Brie, O. Caspary, A. Mohammad-Djafari, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (2004), pp. 485–48
T.O. Virtanen, A.T. Cemgil, S.J. Godsill, in Proceedings of IEEE ICASSP (2008), pp. 1–4
C.J. Lin, IEEE Trans. Neural Netw. 18(6), 1589 (2007)
M. Arngren, M.N. Schmidt, J. Larsen, J. Signal Process. Syst. 65(3), 479 (2010)
D. Zhou, H.Y. Gao, Y.J. Zhang, Adv. Mater. Res. 651, 858 (2013)
K. Stadlthanner, F. Theis, C. Puntonet, J.M. Górriz, A.M. Tomé, E.W. Lang, in ISBMDA. LNCS (LNBI), vol. 3745 (Springer, Heidelberg, 2005), pp. 137–148
A. Cichocki, R. Zdunek, Int. J. Neural Syst. 17(6), 431 (2007)
D.J.C. MacKay, Ensemble learning and evidence maximization. Technical report, Cavendish Laboratory, University of Cambridge (1995)
C.M. Bishop, in Advances in Neural Information Processing Systems NIPS (1999), pp. 382–388
M.N. Schmidt, O. Winther, L.K. Hanse, in International Conference on Independent Component Analysis and Signal Separation. Lecture Notes in Computer Science (LNCS), vol. 5441 (Springer, New York, 2009), pp. 540–547
M. Zhong, M. Girolami, J. Mach. Learn. Res. 5, 663 (2009)
M.N. Schmidt, M. Mørup, in European Signal Processing Conference (EUSIPCO) (2010)
M.I. Jordan, Z. Ghahramani, T.S. Jaakkola, L.K. Saul, Mach. Learn. 37, 183 (1998)
C.M. Bishop, in Proceedings 9-th International Conference on Artificial Neural Networks, ICANN (1999), pp. 509–514
H. Attias, Advances in Neural Information Processing Systems, NIPS 12 (MIT Press, Cambridge, 2000)
Z. Ghahramani, M.J. Beal, Advances in Neural Information Processing Systems NIPS 13 (MIT Press, Cambridge, 2001), pp. 507–513
M. Harva, A. Kabán, Signal Process. 87(3), 509 (2007)
A. Kabán, E. Bingham, Neurocomputing 71(10–12), 2291 (2008)
V.Y.F. Tan, C. Fevotte, in Proceedings of Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS’09) (2009), pp. 1–5
V.Y.F. Tan, C. Fevotte, IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1592 (2013)
M.J. Beal, Z. Ghahramani, Bayesian Anal. 1(4), 793 (2006)
J.L.W.V. Jensen, Acta Math. 30, 175 (1906)
Z. Ghahramani, Advanced Lectures on Machine Learning (2004) pp. 72–112
D.J.C. MacKay, Neural Comput. 4(3), 415 (1992)
D.J.C. MacKay, Maximum Entropy and Bayesian Methods (Kluwer Academic Publishers, Boston, 1996), pp. 43–60
Acknowledgments
Support by Infineon Technologies AG and the DAAD (PPP Luso-Alem\(\tilde{a}\), PPP Hispano-Alemana) is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Schachtner, R., Pöppel, G., Tomé, A.M., Lang, E.W. (2016). From Binary NMF to Variational Bayes NMF: A Probabilistic Approach. In: Naik, G. (eds) Non-negative Matrix Factorization Techniques. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48331-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-48331-2_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48330-5
Online ISBN: 978-3-662-48331-2
eBook Packages: EngineeringEngineering (R0)