Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Robust Soft Semi-supervised Discriminant Projection for Feature Learning

Authors : Xiaoyu Wang, Zhao Zhang, Yan Zhang

Published in: Neural Information Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Image feature extraction and noise/outlier processing has received more and more attention. In this paper, we first take the full use of labeled and unlabeled samples, which leads to a semi-supervised model. Based on the soft label, we combine unlabeled samples with their predicted labels so that all the samples have their own soft labels. Our ratio based model maximizes the soft between-class scatter, as well as minimizes the soft within-class scatter plus a neighborhood preserving item, so that our approach can explicitly extract discriminant and locality preserving features. Further, to make the result be more robust to outliers, all the distance metrics are configured as L1-norm instead of L2-norm. An effective iterative method is taken to solve the optimal function. Finally, we conduct simulation experiments on CASIA-HWDB1.1 and MNIST handwriting digits datasets. The results verified the effectiveness of our approach compared with other related methods.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization
next chapter A Hybrid Pooling Method for Convolutional Neural Networks
Literature
1.
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2012)MATH
2.
He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 2, pp. 1208–1213. IEEE (2005)
3.
Kwak, N.: Principal component analysis based on l1-norm maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30(9), 1672–1680 (2008)CrossRef
4.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
5.
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Online and offline handwritten chinese character recognition: benchmarking on new databases. Pattern Recogn. 46(1), 155–162 (2013)CrossRef
6.
Song, Y., Nie, F., Zhang, C., Xiang, S.: A unified framework for semi-supervised dimensionality reduction. Pattern Recogn. 41(9), 2789–2799 (2008)CrossRefMATH
7.
Sugiyama, M.: Local fisher discriminant analysis for supervised dimensionality reduction. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 905–912. ACM (2006)
8.
Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)CrossRef
9.
Zhang, Z., Chow, T.W., Zhao, M.: M-Isomap: orthogonal constrained marginal isomap for nonlinear dimensionality reduction. IEEE Trans. Cybern. 43(1), 180–191 (2013)CrossRef
10.
Zhang, Z., Yan, S., Zhao, M.: Pairwise sparsity preserving embedding for unsupervised subspace learning and classification. IEEE Trans. Image Process. 22(12), 4640–4651 (2013)MathSciNetCrossRef
11.
Zhang, Z., Zhang, L., Zhao, M., Jiang, W., Liang, Y., Li, F.: Semi-supervised image classification by nonnegative sparse neighborhood propagation. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 139–146. ACM (2015)
12.
Zhao, M., Zhang, Z., Chow, T.W., Li, B.: A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction. Neural Netw. 55, 83–97 (2014)CrossRefMATH
13.
Zhong, F., Zhang, J., Li, D.: Discriminant locality preserving projections based on l1-norm maximization. IEEE Trans. Neural Netw. Learn. Syst. 25(11), 2065–2074 (2014)CrossRef
Metadata
Title
Robust Soft Semi-supervised Discriminant Projection for Feature Learning
Authors
Xiaoyu Wang
Zhao Zhang
Yan Zhang
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_50

Premium Partner

    Image Credits