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International Conference on Collaborative Computing: Networking, Applications and Worksharing

CollaborateCom 2016: Collaborate Computing: Networking, Applications and Worksharing pp 199–210Cite as

LBDAG-DNE: Locality Balanced Subspace Learning for Image Recognition

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Abstract

The cloud-computing environment makes it possible to select the best features when tuning parameters. Various dimensionality reduction algorithms can achieve the best features with the tuning of parameters. Double adjacency graphs-based discriminant neighborhood embedding (DAG-DNE) is a typical graph-based dimensionality reduction method, and has been successfully applied to image recognition. It involves the construction of two adjacency graphs, with the goal of learning the intrinsic structure of the data. However, it may impair the different degrees of importance of the intra-class information and inter-class information of the given data. In this paper, we develop an extension of DAG-DNE, called locality balanced double adjacency graphs-based discriminant neighborhood embedding (LBDAG-DNE) by considering the intra-class information and inter-class information of the given data differently. LBDAG-DNE can find a good projection matrix, which allows neighbors belonging to the same class to be compact while neighbors belonging to different classes become separable in the subspace. Experiments on two image databases illustrate the effectiveness of the proposed approach.

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Notes

  1. 1.

    http://yann.lecun.com/exdb/mnist/.

  2. 2.

    web.mit.edu/emeyers/www/face_databases.html#umist.

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Acknowledgement

This work is supported by the National Science of Foundation of China, under grant No. 61571066 and grant No. 61472047.

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chuntao Ding & Qibo Sun

Authors
  1. Chuntao Ding
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  2. Qibo Sun
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Correspondence to Chuntao Ding .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Ao Zhou

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ding, C., Sun, Q. (2017). LBDAG-DNE: Locality Balanced Subspace Learning for Image Recognition. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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