Abstract
We live in the era of Big Data, or at least our awareness of Big Data’s presence and impact has sharpened in the past ten years. Compared to data characteristics decades ago, Big Data not only means a deluge of unfiltered bytes, but even more importantly it represents a dramatic increase in data dimensionality (the number of variables) and complexity (the relationships among the often interdependent variables, intricacy of cluster structure). Along with the opportunities for nuanced understanding of processes and for decision making, these data created new demands for information extraction methods in terms of the detail that is expected to be identified in analysis tasks such as clustering, classification, regression, and parameter inference. Many traditionally favored techniques do not meet these challenges if one’s aim is to fully exploit the rich information captured by sophisticated sensors and other automated data collection techniques, to ensure discovery of surprising small anomalies, discriminate important, subtle differences, and more. A flurry of technique developments has been spawned, many augmenting existing algorithms with increasingly complex features.
This paper uses ALMA data ADS/JAO.ALMA#2011.0.00465.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
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References
Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Heidelberg (1997)
Zhang, L., Merényi, E., Grundy, W.M., Young, E.Y.: Inference of surface parameters from near-infrared spectra of crystaline h2o ice with neural learning. Publications of the Astronomical Society of the Pacific 122(893), 839–852 (2010), doi:10.1086/655115
Merényi, E., Tasdemir, K., Zhang, L.: Learning highly structured manifolds: harnessing the power of SOMs. In: Biehl, M., Hammer, B., Verleysen, M., Villmann, T. (eds.) Similarity-Based Clustering. LNCS, vol. 5400, pp. 138–168. Springer, Heidelberg (2009)
Tasdemir, K., Merényi, E.: Exploiting data topology in visualization and clustering of Self-Organizing Maps. IEEE Trans. on Neural Networks 20(4), 549–562 (2009)
Mendenhall, M., Merényi, E.: Relevance-based feature extraction for hyperspectral images. IEEE Trans. on Neural Networks 19(4), 658–672 (2008)
Merényi, E., Jain, A., Villmann, T.: Explicit magnification control of self-organizing maps for “forbidden” data. IEEE Trans. on Neural Networks 18(3), 786–797 (2007)
Zhang, L., Merényi, E.: Weighted Differential Topographic Function: A Refinement of the Topographic Function. In: Proc. 14th European Symposium on Artificial Neural Networks (ESANN 2006), Brussels, Belgium, pp. 13–18. D facto publications (2006)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15, 1059–1068 (2002)
Villmann, T., Der, R., Herrmann, M., Martinetz, T.: Topology Preservation in Self–Organizing Feature Maps: Exact Definition and Measurement. IEEE Transactions on Neural Networks 8(2), 256–266 (1997)
Bauer, H.U., Der, R., Herrmann, M.: Controlling the magnification factor of self–organizing feature maps. Neural Computation 8(4), 757–771 (1996)
Martinetz, T., Schulten, K.: Topology representing networks. Neural Networks 7(3), 507–522 (1994)
Ultsch, A., Simeon, H.P.: Kohonen’s self organizing feature map for exploratory data analysis. In: Proc. INNC-1990-PARIS, Paris, vol. I, pp. 305–308 (1990)
DeSieno, D.: Adding a conscience to competitive learning. In: IEEE International Conference on Neural Networks, pp. 117–124. IEEE (1988)
Rudd, L., Merényi, E.: Assessing debris-flow potential by using AVIRIS imagery to map surface materials and stratigraphy in cataract canyon, Utah. In: Green, R. (ed.) Proc. 14th AVIRIS Earth Science and Applications Workshop, Pasadena, CA, May 24-27 (2005)
Merényi, E.: Hyperspectral image analysis in planetary science and astronomy. abstract. Presentation in Special Session “Building the Astronomical Information Sciences: From NASA’s AISR Program to the New AAS Working Group on Astroinformatics and Astrostatistics” (January 7, 2014)
Casassus, S., van der Pas, G., Perez, M.S., et al.: Flowes of gas through a protoplanetary gap. Nature 493, 191 (2013)
O’Driscoll, P.: Using Self-Organizing Maps to discover functinal relationships of brain areas from fMRI images. Master’s thesis, Rice University (June 2014)
Heller, R., Stanley, D., Yekutieli, D., Rubin, N., Benjamini, Y.: Cluster-based analysis of FMRI data. NeuroImage 33(2), 599–608 (2006) PMID: 16952467
Liao, W., Chen, H., Yang, Q., Lei, X.: Analysis of fMRI data using improved self-organizing mapping and spatio-temporal metric hierarchical clustering. IEEE Transactions on Medical Imaging 27(10), 1472–1483 (2008)
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Merényi, E. (2014). The Sky Is Not the Limit. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_17
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DOI: https://doi.org/10.1007/978-3-319-07695-9_17
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