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Information Fusion Based on Gaussian Process Latent Variable Model

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Information Fusion

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Abstract

Gaussian Process Latent Variable Model (GPLVM) is capable of representing the data without a determined function, which is a generative and non-parametric model. Compared with sparse/collaborative representation, GPLVM enjoys the non-linearity, which does exist in real-world datasets. This chapter proposes three GPLVM based information fusion methods, contributing to the classification performance improvement. After reading this chapter people can have preliminary knowledge on GPLVM based fusion algorithms.

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References

  1. Akaho S. A kernel method for canonical correlation analysis. arXiv preprint cs/0609071. 2006.

    Google Scholar 

  2. Fukumizu K, Bach FR, Gretton A. Statistical consistency of kernel canonical correlation analysis. J Mach Learn Res. 2007;8(Feb):361–383.

    MathSciNet  MATH  Google Scholar 

  3. Lai PL, Fyfe C. Kernel and nonlinear canonical correlation analysis. Int J Neural Syst. 2000;10(05):365–377.

    Article  Google Scholar 

  4. Lopez-Paz D, Sra S, Smola A, Ghahramani Z, Schölkopf B. Randomized nonlinear component analysis. In: International conference on machine learning. 2014. p. 1359–1367.

    Google Scholar 

  5. Eleftheriadis S, Rudovic O, Pantic M. Discriminative shared gaussian processes for multiview and view-invariant facial expression recognition. IEEE Trans Image Process. 2015;24(1):189–204.

    Article  MathSciNet  Google Scholar 

  6. Lawrence ND. Gaussian process latent variable models for visualisation of high dimensional data. Adv Neural Inf Process Syst. 2004;16(3):329–336.

    Google Scholar 

  7. Ek CH, Lawrence PHTND. Shared Gaussian process latent variable models. PhD Thesis. 2009.

    Google Scholar 

  8. Urtasun R, Darrell T. Discriminative Gaussian process latent variable model for classification. In: Proceedings of the 24th international conference on machine learning. New York: ACM; 2007. p. 927–934.

    Chapter  Google Scholar 

  9. Rasmussen CE. Gaussian processes for machine learning. Cambridge: MIT Press; 2006.

    MATH  Google Scholar 

  10. Song G, Wang S, Huang Q, Tian Q. Similarity gaussian process latent variable model for multi-modal data analysis. In: Proceedings of the IEEE international conference on computer vision. 2015. p. 4050–4058.

    Google Scholar 

  11. Lawrence ND, Quiñonero-Candela J. Local distance preservation in the GP-LVM through back constraints. In: Proceedings of the 23rd international conference on machine learning. New York: ACM; 2006. p. 513–520.

    Chapter  Google Scholar 

  12. Jiang X, Gao J, Hong X, Cai Z. Gaussian processes autoencoder for dimensionality reduction. In: Pacific-Asia conference on knowledge discovery and data mining. Berlin: Springer; 2014. p. 62–73.

    Chapter  Google Scholar 

  13. Ng A. Sparse autoencoder. CS294A Lecte Notes. 2011;72:1–19.

    Google Scholar 

  14. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY. Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11). 2011. p. 689–696.

    Google Scholar 

  15. Li J, Zhang B, Zhang D. Shared autoencoder gaussian process latent variable model for visual classification. IEEE Trans Neural Netw Learn Syst. 2017;29:4272–4286.

    Article  Google Scholar 

  16. Li J, Zhang B, Lu G, Ren H, Zhang D. Visual classification with multikernel shared gaussian process latent variable model. IEEE Trans Cybern. 2018;49(8):2886–2899.

    Article  Google Scholar 

  17. Li J, Lu G, Zhang B, You J, Zhang D. Shared linear encoder-based multikernel gaussian process latent variable model for visual classification. IEEE Transactions Cybern. 2019;51:534–547.

    Article  Google Scholar 

  18. Chung FRK. Spectral graph theory, vol. 92. Providence: American Mathematical Society; 1997.

    MATH  Google Scholar 

  19. Zhong G, Li W-J, Yeung D-Y, Hou X, Liu C-L et al. Gaussian process latent random field. In: Association for the advancement of artificial intelligence. 2010.

    Google Scholar 

  20. Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Du B. Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognit. 2015;48(10):3102–3112.

    Article  Google Scholar 

  21. Salzmann M, Urtasun R. Implicitly constrained gaussian process regression for monocular non-rigid pose estimation. In: Advances in neural information processing systems. 2010. p. 2065–2073.

    Google Scholar 

  22. Rasiwasia N, Pereira JC, Coviello E, Doyle G, Lanckriet GRG , Levy R, Vasconcelos N. A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM international conference on multimedia. 2010. p. 251–260.

    Google Scholar 

  23. Kemp C, Tenenbaum JB, Griffiths TL, Yamada T, Ueda N. Learning systems of concepts with an infinite relational model. In: AAAI, vol. 3. 2006. p. 5.

    Google Scholar 

  24. Lampert CH, Nickisch H, Harmeling S. Learning to detect unseen object classes by between-class attribute transfer. In: CVPR 2009. IEEE conference on computer vision and pattern recognition, 2009. Piscataway: IEEE; 2009. p. 951–958..

    Google Scholar 

  25. Lampert CH, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Analy Mach Intell. 2014;36(3):453–465.

    Article  Google Scholar 

  26. Chua T-S, Tang J, Hong R, Li H, Luo Z, Zheng Y. NUS-WIDE: a real-world web image database from national university of Singapore. In: Proceedings of the ACM international conference on image and video retrieval. New York: ACM; 2009. p. 48.

    Google Scholar 

  27. Thompson B. Canonical correlation analysis. In: Encyclopedia of statistics in behavioral science. Hoboken: Wiley; 2005.

    Google Scholar 

  28. Hardoon DR, Szedmak S, Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 2004;16(12):2639–2664.

    Article  Google Scholar 

  29. Li J, Zhang D, Li Y, Wu J, Zhang B. Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection. Inf Sci. 2016;384.

    Google Scholar 

  30. Andrew G, Arora R, Bilmes J, Livescu K. Deep canonical correlation analysis. In: International conference on machine learning. 2013. p. 1247–1255.

    Google Scholar 

  31. Wang W, Arora R, Livescu K, Bilmes J. On deep multi-view representation learning. In: International conference on machine learning. 2015. p. 1083–1092.

    Google Scholar 

  32. Li J, Zhang B, Ren H, Zhang D. Visual classification with multi-kernel shared Gaussian process latent variable model. IEEE Trans Cybern. 2018;49:1–14.

    Google Scholar 

  33. Lawrence N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J Mach Learn Res. 2005;6(Nov):1783–1816.

    MathSciNet  MATH  Google Scholar 

  34. Gao X, Wang X, Tao D, Li X. Supervised Gaussian process latent variable model for dimensionality reduction. IEEE Trans Syst Man Cybern Part B. 2011;41(2):425–434.

    Article  Google Scholar 

  35. Li S, Fu Y. Learning robust and discriminative subspace with low-rank constraints. IEEE Trans Neural Netw Learn Syst. 2015;27(11):2160-2173.

    Article  MathSciNet  Google Scholar 

  36. Spielman D. Spectral graph theory. Lecture notes, yale university. 2009. p. 740–0776.

    Google Scholar 

  37. Li J, Lu G, Zhang B, You J, Zhang D. Shared linear encoder-based multikernel gaussian process latent variable model for visual classification. IEEE Trans Cybern. 2019;51:534–547.

    Article  Google Scholar 

  38. Kan M, Shan S, Zhang H, Lao S, Chen X. Multi-view discriminant analysis. IEEE Trans Pattern Analy Mach Intelll. 2016;38(1):188–194.

    Article  Google Scholar 

  39. Song G, Wang S, Huang Q, Tian Q. Multimodal similarity gaussian process latent variable model. IEEE Trans Image Process. 2017;26(9):4168–4181.

    Article  MathSciNet  Google Scholar 

  40. Song G, Wang S, Huang Q, Tian Q. Multimodal gaussian process latent variable models with harmonization. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 5029–5037.

    Google Scholar 

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Authors and Affiliations

  1. Harbin Institute of Technology, Shenzhen School of Computer Science and Technology Xili, Nanshan, Shenzhen, China

    Jinxing Li

  2. Department of Computer and Information Science, The University of Macau, Macau, China

    Bob Zhang

  3. School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

    David Zhang

Authors
  1. Jinxing Li
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  2. Bob Zhang
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  3. David Zhang
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Li, J., Zhang, B., Zhang, D. (2022). Information Fusion Based on Gaussian Process Latent Variable Model. In: Information Fusion. Springer, Singapore. https://doi.org/10.1007/978-981-16-8976-5_3

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  • DOI: https://doi.org/10.1007/978-981-16-8976-5_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8975-8

  • Online ISBN: 978-981-16-8976-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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