Extracting online information from dual and multiple data streams

  • Zeeshan Khawar Malik
  • Amir Hussain
  • Q. M. Jonathan Wu
Original Article
  • 46 Downloads

Abstract

In this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by developing an online version of the CCA and apply it to reservoirs of an echo state network in order to capture shared temporal information in two data streams. We further develop the proposed method by forcing it to ignore shared information that is created from static values using derivative information. We finally develop a novel multi-set CCA method which can identify shared information in more than two data streams simultaneously. The comparative effectiveness of the proposed methods is illustrated using artificial and real benchmark datasets.

Keywords

Canonical correlation analysis Echo state network Generalized eigenvalue problem High-variance feature-extraction Neural network Unsupervised learning 

References

  1. 1.
    Akaho S, kernal A (2006) method for canonical correlation analysis. In: Proceedings of the international meeting of the Psychometric Society (IMPS2001), Springer (arXiv preprint cs/0609071)Google Scholar
  2. 3.
    BieBmann F, Meinecke FC, Gretton A, Rauch A, Rainer G, Logothetis NK, Muller KR (2010) Temporal kernal CCA and its application in multimodal neuronal data analysis. Mach Learn 79(1–2):5–27MathSciNetCrossRefGoogle Scholar
  3. 2.
    Cambria E, Fu J, Bisio F, Poria S (2015) AffectiveSpace2: enabling affective intuition for concept-level sentiment analysis. In: AAAI, pp 508–514Google Scholar
  4. 4.
    Cambria E, Gastaldo P, Bisio F, Zunino R (2015) An ELM-based model for affective analogical reasoning. Neurocomputing 149(Part A):443–455Google Scholar
  5. 5.
    Friman O, Borga M, Lundberg P, Knutsson H (2002) Exploratory fmri analysis by autocorrelation maximization. Neuroimage 16(2):454–464CrossRefGoogle Scholar
  6. 6.
    Gastaldo P, Zunino R, Cambria E, Decherchi S (2013) Combining elm with random projections. IEEE Intell Syst 28(6):46–48Google Scholar
  7. 7.
    Gou Z, Fyfe C (2001) A family of networks which perform canonical correlation analysis. Int J Knowl Based Intell Eng Syst 5(2):76–82Google Scholar
  8. 8.
    Gou Z, Fyfe C (2003) A canonical correlation neural network for multicollinearity and functional data. Neural Netw 17(2):285–293CrossRefMATHGoogle Scholar
  9. 9.
    Gros C (2009) Cognitive computation with autonomously active neural networks: an emerging field. Cognit Comput 1(1):77CrossRefGoogle Scholar
  10. 10.
    Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefMATHGoogle Scholar
  11. 11.
    Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3/4):321–377CrossRefMATHGoogle Scholar
  12. 12.
    Huang GB (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cognit Comput 6(3):376CrossRefGoogle Scholar
  13. 13.
    Kettenring JR (1971) Canonical analysis of several sets of variables. Biometrika 58(3):433MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Lai PL, Fyfe C (1999) A neural implementation of canonical correlation analysis. Neural Netw 12(10):1391–1397CrossRefGoogle Scholar
  15. 15.
    Lai PL, Fyfe C (2001) Kernel and nonlinear canonical correlation analysis. Int J Neural Syst 10(5):365–377CrossRefGoogle Scholar
  16. 16.
    LeCun Y, Cortes C (1998) The MNIST database of handwritten digits. The dataset is available at http://yann.lecun.com/exdb/mnist
  17. 17.
    Lemire D (2009) Faster retrieval with a two-pass dynamic-time-warping lower bound. Pattern Recognit 42(9):2169CrossRefMATHGoogle Scholar
  18. 18.
    Lukosevicius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127–149CrossRefMATHGoogle Scholar
  19. 19.
    Malik ZK, Hussain A, Wu J (2014) Novel biologically inspired approaches to extracting online information from temporal data. Cognit Comput 6(3):1–13CrossRefGoogle Scholar
  20. 33.
    Malik ZK, Hussain A, Wu J (2016) An online generalized eigenvalue version of Laplacian eigenmap for visual big data. Neurocomputing 173(2):127–136CrossRefGoogle Scholar
  21. 34.
    Malik ZK, Hussain A, Wu QJ (2016) Multilayered echo state machine: a novel architecture and algorithm. IEEE Trans Cybern PP(99):1–14Google Scholar
  22. 20.
    Mardia KV, Kent JT, Bibby JM (1980) Multivariate analysis. Academic Press, CambridgeGoogle Scholar
  23. 21.
    Nielsen AA (2002) Multi-set canonical correlations analysis and multispectral, truely multitemporal remote sensing data. IEEE Trans Image Process 11(3):293CrossRefGoogle Scholar
  24. 22.
    Poria S, Gelbukh A, Cambria E, Hussain A, Huang GB (2014) Emosenticspace: a novel framework for affective common-sense reasoning. Knowl Based Syst 69:108–123. doi:10.1016/j.knosys.2014.06.011 CrossRefGoogle Scholar
  25. 23.
    Schrauwen B, Verstraeten D, Van Campenhout J (2007) An overview of reservoir computing: theory, application and implementation. In: Proceedings of the 15th European symposium on artificial neural networks, pp 471–482Google Scholar
  26. 24.
    Steil JJ (2007) Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Netw 20(3):353–364CrossRefMATHGoogle Scholar
  27. 25.
    Via J, Santamaria I, Perez J (2007) A learning algorithm for adaptive canonical correlation analysis of several data sets. Neural Netw 20(1):139–152CrossRefMATHGoogle Scholar
  28. 26.
    Wang X, Crowe M, Fyfe C (2012) Dual stream data exploration. Int J Data Min Model Manag 4(2):188–202CrossRefGoogle Scholar
  29. 27.
    Wiskott L, Sejnowski TJ (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14(4):715–770CrossRefMATHGoogle Scholar
  30. 28.
    Wollmer M, Eyben F, Graves A, Schuller B, Rigoll G (2010) Bidirectional lstm networks for context-sensitive keyword detection in a cognitive virtual agent framework. Cognit Comput 2(3):180CrossRefGoogle Scholar
  31. 29.
    Yang Y, Wu QMJ, Wang Y, Zeeshan KM, Lin X, Yuan X (2014) Data partition learning with multiple extreme learning machines. IEEE Trans Cybern. PP(99):18–20. doi:10.1109/TCYB.2014.2352594
  32. 30.
    Yger F, Berar M, Gasso G, Rakotomamonjy A (2012) Adaptive canonical correlation analysis based on matrix manifolds. In: Proceedings of the international conference on machine learning. New york, NY, ACMGoogle Scholar
  33. 31.
    Zeck G, Bethge M, Macke JH (2008) Receptive fields without spike-triggering. In: Platt JC, Koller D, Singer Y, Roweis ST (eds) Advances in neural information processing systems 20. Curran Associates, Inc, pp 969–976Google Scholar
  34. 32.
    Zhang Q, Leung YW (2000) A class of learning algorithms for principal component analysis and minor component analysis. IEEE Trans Neural Netw 11(2):200–204CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Zeeshan Khawar Malik
    • 1
  • Amir Hussain
    • 1
  • Q. M. Jonathan Wu
    • 2
  1. 1.University of StirlingStirlingScotland, UK
  2. 2.University of WindsorWindsor Canada

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