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Modular Dimensionality Reduction

  • Henry W. J. ReeveEmail author
  • Tingting Mu
  • Gavin Brown
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11051)

Abstract

We introduce an approach to modular dimensionality reduction, allowing efficient learning of multiple complementary representations of the same object. Modules are trained by optimising an unsupervised cost function which balances two competing goals: Maintaining the inner product structure within the original space, and encouraging structural diversity between complementary representations. We derive an efficient learning algorithm which outperforms gradient based approaches without the need to choose a learning rate. We also demonstrate an intriguing connection with Dropout. Empirical results demonstrate the efficacy of the method for image retrieval and classification.

Keywords

Ensemble learning Dimensionality reduction Dropout Kernel principal components analysis 

Notes

Acknowledgments

H. Reeve was supported by the EPSRC through the Centre for Doctoral Training Grant [EP/1038099/1]. G. Brown was supported by the EPSRC LAMBDA project [EP/N035127/1].

Supplementary material

478880_1_En_37_MOESM1_ESM.pdf (1 mb)
Supplementary material 1 (pdf 1058 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of BirminghamBirminghamUK
  2. 2.University of ManchesterManchesterUK

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