J. Space Weather Space Clim.
Volume 6, 2016
Statistical Challenges in Solar Information Processing
Article Number A3
Number of page(s) 19
Published online 25 January 2016
  • Ahmed, O.W., R. Qahwaji, T. Colak, P.A. Higgins, P.T. Gallagher, and D.S. Bloomfield. Solar flare prediction using advanced feature extraction, machine learning, and feature selection. Sol. Phys., 283, 157–175, 2013, DOI: 10.1007/s11207-011-9896-1. [NASA ADS] [CrossRef] [Google Scholar]
  • Barnes, G., K.D. Leka, E.A. Schumer, and D.J. Della-Rose. Probabilistic forecasting of solar flares from vector magnetogram data. Space Weather, 5, S09002, 2007, DOI: 10.1029/2007SW000317. [CrossRef] [Google Scholar]
  • Bazot, C., N. Dobigeon, J.-Y. Tourneret, A. Zaas, G. Ginsburg, and A.O. HeroIII. Unsupervised Bayesian linear unmixing of gene expression microarrays. BMC Bioinf., 14 (1), 99, 2013, DOI: 10.1186/1471-2105-14-99. [CrossRef] [Google Scholar]
  • Bhattacharyya, A. On a measure of divergence between two multinomial populations. Sankhya, 7 (4), 401–406, 1946. [Google Scholar]
  • Bioucas-Dias, J.M., A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Topics Appl. Earth Observations Remote Sensing, 5 (2), 354–379, 2012. [Google Scholar]
  • Bobra, M.G., and S. Couvidat. Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. Astrophys. J., 798, 135, 2015, DOI: 10.1088/0004-637X/798/2/135. [Google Scholar]
  • Colak, T., and R. Qahwaji. Automated McIntosh-based classification of sunspot groups using MDI images. Sol. Phys., 248, 277–296, 2008, DOI: 10.1007/s11207-007-9094-3. [Google Scholar]
  • Colak, T., and R. Qahwaji. Automated solar activity prediction: a hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares. Space Weather, 7, S06001, 2009, DOI: 10.1029/2008SW000401. [NASA ADS] [CrossRef] [Google Scholar]
  • Comon, P., and C. Jutten. Handbook of Blind Source Separation: Independent Component Analysis and Blind Deconvolution. Academic Press, Oxford, 2010. [Google Scholar]
  • Csiszar, I. Information-type measures of difference of probability distributions and indirect observations. Studia Sci. Math. Hungar., 2, 299–318, 1967. [Google Scholar]
  • DeForest, C. On re-sampling of solar images. Sol. Phys., 219 (1), 3–23, 2004. [NASA ADS] [CrossRef] [Google Scholar]
  • Ding, C., T. Li, and M.I. Jordan. Convex and semi-nonnegative matrix factorizations. IEEE Trans. Pattern Anal. Mach. Intell., 32 (1), 45–55, 2010. [Google Scholar]
  • Dudok de Wit, T., S. Moussaoui, C. Guennou, F. Auchère, G. Cessateur, M. Kretzschmar, L.A. Vieira, and F.F. Goryaev. Coronal temperature maps from solar EUV images: a blind source separation approach. Sol. Phys., 283, 31–47, 2013, DOI: 10.1007/s11207-012-0142-2. [CrossRef] [Google Scholar]
  • Edelman, A., T.A. Arias, and S.T. Smith. The geometry of algorithms with orthogonality constraints. SIAM J. Matrix Anal. Appl., 20 (2), 303–353, 1998. [CrossRef] [Google Scholar]
  • Falconer, D.A., R.L. Moore, and G.A. Gary. Magnetogram measures of total nonpotentiality for prediction of solar coronal mass ejections from active regions of any degree of magnetic complexity. Astrophys. J., 689, 1433–1442, 2008, DOI: 10.1086/591045. [NASA ADS] [CrossRef] [Google Scholar]
  • Galluccio, L., O. Michel, P. Comon, M. Kliger, and A.O. HeroIII. Clustering with a new distance measure based on a dual-rooted tree. Inform. Sciences, 251, 96–113, 2013. [CrossRef] [Google Scholar]
  • Georgoulis, M.K., and D.M. Rust. Quantitative forecasting of major solar flares. Astrophys. J. Lett., 661, L109–L112, 2007, DOI: 10.1086/518718. [Google Scholar]
  • Guo, J., H. Zhang, O.V. Chumak, and Y. Liu. A quantitative study on magnetic configuration for active regions. Sol. Phys., 237, 25–43, 2006, DOI: 10.1007/s11207-006-2081-2. [NASA ADS] [CrossRef] [Google Scholar]
  • Győri, L., T. Baranyi, and A. Ludmány. Photospheric data programs at the Debrecen observatory. Proc. Int. Astron. Union, 6 (S273), 403–407, 2010. [CrossRef] [Google Scholar]
  • Hale, G.E., F. Ellerman, S.B. Nicholson, and A.H. Joy. The magnetic polarity of sun-spots. Astrophys. J., 49, 153, 1919, DOI: 10.1086/142452. [CrossRef] [Google Scholar]
  • Hellinger, E. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die reine und angewandte Mathematik, 136, 210–271, 1909. [Google Scholar]
  • Higgins, P.A., P.T. Gallagher, R. McAteer, and D.S. Bloomfield. Solar magnetic feature detection and tracking for space weather monitoring. Adv. Space Res., 47 (12), 2105–2117, 2011. [NASA ADS] [CrossRef] [Google Scholar]
  • Huang, X., D. Yu, Q. Hu, H. Wang, and Y. Cui. Short-term solar flare prediction using predictor teams. Sol. Phys., 263, 175–184, 2010, DOI: 10.1007/s11207-010-9542-3. [NASA ADS] [CrossRef] [Google Scholar]
  • Jolliffe, I.T. Principal Component Analysis, 2nd ed., Springer-Verlag New York, Inc., New York, 2002. [Google Scholar]
  • Kruskal, J.B., and M. Wish. Multidimensional Scaling, vol. 11, Sage, New York, 1978. [CrossRef] [Google Scholar]
  • Kullback, S., and R.A. Leibler. On information and sufficiency. Ann. Math. Stat., 22, 79–86, 1951. [CrossRef] [Google Scholar]
  • Künzel, H. Die Flare-Häufigkeit in Fleckengruppen unterschiedlicher Klasse und magnetischer Struktur. Astron. Nachr., 285, 271–271, 1960. [NASA ADS] [CrossRef] [Google Scholar]
  • Langville, A.N., C.D. Meyer, R. Albright, J. Cox, and D. Duling. Initializations for the nonnegative matrix factorization, in Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, Citeseer, 2006. [Google Scholar]
  • Lee, D.D., and H.S. Seung. Algorithms for non-negative matrix factorization, in Advances in Neural Information Processing Systems (NIPS), 556–562, 2001. [Google Scholar]
  • Lee, K., Y.-J. Moon, J.-Y. Lee, K.-S. Lee, and H. Na. Solar flare occurrence rate and probability in terms of the sunspot classification supplemented with sunspot area and its changes. Sol. Phys., 281, 639–650, 2012, DOI: 10.1007/s11207-012-0091-9. [CrossRef] [Google Scholar]
  • Leka, K.D., and G. Barnes. Photospheric magnetic field properties of flaring vs. flare-quiet active regions III: discriminant analysis of a statistically significant database. In American Astronomical Society Meeting Abstracts #204, vol. 36 of Bulletin of the American Astronomical Society, 715, 2004. [Google Scholar]
  • Lin, C.-J. Projected gradient methods for nonnegative matrix factorization. Neural Comput., 19 (10), 2756–2779, 2007. [CrossRef] [Google Scholar]
  • Mayfield, E.B., and J.K. Lawrence. The correlation of solar flare production with magnetic energy in active regions. Sol. Phys., 96, 293–305, 1985, DOI: 10.1007/BF00149685. [CrossRef] [Google Scholar]
  • Mittelman, R., N. Dobigeon, and A. Hero. Hyperspectral image unmixing using a multiresolution sticky HDP. IEEE Trans. Signal Process., 60 (4), 1656–1671, 2012, DOI: 10.1109/TSP.2011.2180718. [CrossRef] [Google Scholar]
  • Moon, K.R., and A.O. HeroIII. Ensemble estimation of multivariate f-divergence, in Information Theory (ISIT), 2014 IEEE International Symposium on, Honolulu, USA, IEEE, 356–360, 2014a. [Google Scholar]
  • Moon, K.R., and A.O. HeroIII. Multivariate f-divergence estimation with confidence. Adv. Neural Inf. Process. Syst., 27, 2420–2428, 2014b. [Google Scholar]
  • Moon, K.R., J.J. Li, V. Delouille, F. Watson, and A.O. HeroIII. Image patch analysis and clustering of sunspots: a dimensionality reduction approach, in IEEE International Conference on Image Processing (ICIP), Paris, France, IEEE, 1623–1627, 2014. [Google Scholar]
  • Moon, K.R., J.J. Li, V. Delouille, R. De Visscher, F. Watson, and A.O. HeroIII. Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis. J. Space Weather Space Clim., 2015. [Google Scholar]
  • Moon, T.K., and W.C. Stirling. Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, New York, 2000. [Google Scholar]
  • Prim, R.C. Shortest connection networks and some generalizations. Bell Syst. Tech. J., 36 (6), 1389–1401, 1957. [Google Scholar]
  • Ramírez, I., and G. Sapiro. An MDL framework for sparse coding and dictionary learning. IEEE Trans. Signal Process., 60 (6), 2913–2927, 2012. [CrossRef] [Google Scholar]
  • Rand, W.M. Objective criteria for the evaluation of clustering methods. J. Amer. Statist. Assoc., 66 (336), 846–850, 1971. [Google Scholar]
  • Rousseeuw, P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53–65, 1987. [Google Scholar]
  • Sammis, I., F. Tang, and H. Zirin. The dependence of large flare occurrence on the magnetic structure of sunspots. Astrophys. J., 540, 583–587, 2000, DOI: 10.1086/309303. [Google Scholar]
  • Scherrer, P.H., R.S. Bogart, R.I. Bush, J.T. Hoeksema, A.G. Kosovichev, et al. The solar oscillations investigation – Michelson Doppler imager. Sol. Phys., 162, 129–188, 1995, DOI: 10.1007/BF00733429. [Google Scholar]
  • Schrijver, C.J. A characteristic magnetic field pattern associated with all major solar flares and its use in flare forecasting. Astrophys. J. Lett., 655, L117–L120, 2007, DOI: 10.1086/511857. [CrossRef] [Google Scholar]
  • Seichepine, N., S. Essid, C. Févotte, and O. Cappé. Soft nonnegative matrix co-factorization. IEEE Trans. Signal Process., 62 (22), 5940–5949, 2014. [CrossRef] [Google Scholar]
  • Sethian, J.A. A fast marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci., 93, 1591–1595, 1995. [Google Scholar]
  • Song, H., C. Tan, J. Jing, H. Wang, V. Yurchyshyn, and V. Abramenko. Statistical assessment of photospheric magnetic features in imminent solar flare predictions. Sol. Phys., 254, 101–125, 2009, DOI: 10.1007/s11207-008-9288-3. [NASA ADS] [CrossRef] [Google Scholar]
  • Stenning, D.C., T.C.M. Lee, D.A. van Dyk, V. Kashyap, J. Sandell, and C.A. Young. Morphological feature extraction for statistical learning with applications to solar image data. Stat. Anal. Data Min., 6 (4), 329–345, 2013, DOI: 10.1002/sam.11200. [Google Scholar]
  • Stewart, G.W. Error and perturbation bounds for subspaces associated with certain eigenvalue problems. SIAM Rev., 15 (4), 727–764, 1973. [CrossRef] [Google Scholar]
  • Warwick, C.S. Sunspot configurations and proton flares. Astrophys. J., 145, 215, 1966, DOI: 10.1086/148755. [Google Scholar]
  • Watson, F.T., L. Fletcher, and S. Marshall. Evolution of sunspot properties during solar cycle 23. Astron. Astrophys., 533, A14, 2011, DOI: 10.1051/0004-6361/201116655. [CrossRef] [EDP Sciences] [Google Scholar]
  • Yaghoobi, M., T. Blumensath, and M.E. Davies. Dictionary learning for sparse approximations with the majorization method, IEEE Trans. Signal Process., 57 (6), 2178–2191, 2009. [CrossRef] [Google Scholar]
  • Yokoya, N., T. Yairi, and A. Iwasaki. Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Trans. Geosci. Remote Sens., 50 (2), 528–537, 2012. [CrossRef] [Google Scholar]
  • Yu, D., X. Huang, H. Wang, Y. Cui, Q. Hu, and R. Zhou. Short-term solar flare level prediction using a Bayesian network approach. Astrophys. J., 710, 869–877, 2010, DOI: 10.1088/0004-637X/710/1/869. [Google Scholar]

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