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2012 | Vol. 2, No. 3 | 215--222
Tytuł artykułu

Reservoir computing and data visualisation

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We consider the problem of visualisation of high dimensional multivariate time series. A data analyst in creating a two dimensional projection of such a time series might hope to gain some intuition into the structure of the original high dimensional data set. We review a method for visualising time series data using an extension of Echo State Networks (ESNs).The method uses the multidimensional scaling criterion in order to create a visualisation of the time series after its representation in the reservoir of the ESN. We illustrate the method with two dimensional maps of a financial time series. The method is then compared with a mapping which uses a fixed latent space and a novel objective function.
Wydawca

Rocznik
Strony
215--222
Opis fizyczny
Bibliogr. 25 poz., rys.
Twórcy
autor
autor
  • Department of Industrial Engineering and Management, Cheng Shiu University, Taiwan, ROC, tw952276@gmail.com
autor
Bibliografia
  • [1] W. Barbakh, M. Crowe, and C. Fyfe. A family of novel clustering algorithms. In 7th international conference on intelligent data engineering and auto- mated learning, IDEAL2006, pages 283-290, Springer. ISSN 0302-9743, 2006.
  • [2] W. Barbakh, and C. Fyfe. Local vs global interactions in clustering algorithms: Advances over kmeans. International Journal of Knowledge-based and Intelligent Engineering Systems, ISSN 1327-2314, 12(2):83–99, 2008.
  • [3] W. Barbakh, Y. Wu, and C. Fyfe. Non-standard exploratory data analysis. Springer, 2009.
  • [4] S. Basterrech, C. Fyfe, and G. Rubino. Initializing echo state networks with topographic maps. In 2nd International Conference on Morphological Computation (ICMC 2011)., 2011.
  • [5] Christopher M. Bishop, Markus Svensèn, and Christopher K. I. Williams. Gtm: The generative topographic mapping. Neural Computation, 10(1):215–234, 1998.
  • [6] Christopher M. Bishop, Markus Svensèn, and Christopher K. I. Williams. Developments of the generative topographic mapping. Neurocomputing, 21(1):203–224, 1998.
  • [7] Christopher M. Bishop, Markus Svensèn, and Christopher K. I. Williams. Gtm: A principle alternative to the self-organizing map. In Advances in neural information processing systems, 5:354–360, 1997.
  • [8] C. Fyfe. A scale invariant feature map. Network: Computation in Neural Systems, 7:269–275, 1996.
  • [9] C. Fyfe. Hebbian Learning and Negative Feedback Artificial Neural Networks. Springer, 2004.
  • [10] C. Fyfe. Two topographic maps for data visualization. Data Mining and Knowledge Discovery, 14:207–224, 2007. ISSN 1384-5810.
  • [11] Herbert Jaeger. The echo state approach to analysing and training recurrent neural networks. Technical Report 148, German National Research Center for Information Technology, 2001.
  • [12] T. Kohonen. Self-Organising Maps. Springer, 1995.
  • [13] Teuvo Kohonen. Self-Organizing Maps, volume 30. Springer Series in Information Sciences, third edition, 2001.
  • [14] Mantas Lukoševičius. On self-organizing reservoirs and their hierarchies. Technical Report 25, Jacobs University, Bremen, 2010.
  • [15] Mantas Lukoševičius and Hebert Jaeger. Reservoir computing approaches to recurrent neural network training. Computer Science Review, 3:127–149, 2009.
  • [16] Ian Nabney. Netlab. Springer, 2001.
  • [17] Ali Rodan and Peter Ti˘no. Minimum complexity echo state network. IEEE Transactions on Neural Networks, 22:131–44, 2011.
  • [18] R. J. Shiller. Irrational Exuberance. Princeton University Press, 2000,2005.
  • [19] J.. Sun. Extending metric multidimensional scaling with Bregman divergences. PhD thesis, School of Computing, University of the West of Scotland, 2011.
  • [20] J. Sun, M. Crowe, and C. Fyfe. Extending metric multidimensional scaling with bregman divergences. Pattern Recognition, (44):1137–1154, 2011.
  • [21] Michael E. Tipping. Topographic mappings and feed-forward neural networks. PhD thesis, The University of Aston in Birmingham, 1996.
  • [22] T. D. Wang and C. Fyfe, Training Echo State Networks with Neuroscale Proceedings of the International Conference on Technologies and Applications of Artificial Intelligence, 2011 )
  • [23] T. D. Wang and X. Wu and C. Fyfe, Comparative study of visualisation methods for temporal data 2012 IEEE Congress on Evolutionary Computation, 2012
  • [24] T. D. Wang and C. Fyfe. The role of structure size and sparsity in echo state networks for visualisation The United Kingdom Conference on Computational Intelligence, 2011.
  • [25] X.Wang, M. Crowe, and C. Fyfe. Dual stream data exploration. International Journal of Data Mining, Modelling and Management, 2011.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-d690f05c-5ffc-4e35-97cc-cb388f6722e5
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