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A Self-Organising Multi-Manifold Learning Algorithm

  • Conference paper
Bioinspired Computation in Artificial Systems (IWINAC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9108))

Abstract

This paper presents a novel self-organising multi-manifold learning algorithm to extract multiple nonlinear manifolds from data. Extracting these sub-manifolds or manifold structure in the data can facilitate the analysis of large volume of data and discover their underlying patterns and generative causes. Many real data sets exhibit multiple sub-manifold structures due to multiple variations as well as multiple modalities. The proposed learning scheme can learn to establish the intrinsic manifold structure of the data. It can be used in either unsupervised or semi-supervised learning environment where ample unlabelled data can be effectively utilized. Experimental results on both synthetic and real-world data sets demonstrate its effectiveness, efficiency and promising potentials in many big data applications.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK

    Hujun Yin & Shireen Mohd Zaki

Authors
  1. Hujun Yin
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  2. Shireen Mohd Zaki
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Corresponding author

Correspondence to Hujun Yin .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Universidad Politécnica de Cartagena, Cartagena, Spain

    Fco. Javier Toledo-Moreo

  5. Dept. Civil & Environmental Engin. Geodetic Science, Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

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© 2015 Springer International Publishing Switzerland

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Yin, H., Zaki, S.M. (2015). A Self-Organising Multi-Manifold Learning Algorithm. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_41

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  • DOI: https://doi.org/10.1007/978-3-319-18833-1_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18832-4

  • Online ISBN: 978-3-319-18833-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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