Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Safety Traceability System of Livestock and Poultry Industrial Chain Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Robust Manifold Learning Based Ordinal Discriminative Correlation Regression

verfasst von : Qing Tian, Wenqiang Zhang, Liping Wang

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Canonical correlation analysis (CCA) is a typical learning paradigm of capturing the correlation components across multi-views of the same data. When countered with such data with ordinal labels, the accuracy performance yielded by traditional CCA is usually not desirable because of ignoring the ordinal relationships among data labels. In order to incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA (OR-DisCCA) was presented. Although OR-DisCCA can yield better ordinal regression results, its performance will be deteriorated when the data are corrupted with outliers because the ordered class centers easily tend to be biased by the outliers. To address this issue, in this work we construct robust manifold ordinal discriminative correlation regression (rmODCR) by replacing the traditional (\(l_2\)-norm) class centers with \(l_p\)-norm centers in objective optimization. Finally, we experimentally evaluate the effectiveness of the proposed method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Research on Image Classification of Marine Pollutants with Convolution Neural Network
Nächstes Kapitel Signal Subtle Feature Extraction Algorithm Based on Improved Fractal Box-Counting Dimension
Literatur
1.
Breukelen, M.V., Duin, R.P.W., Tax, D.M.J., Hartog, J.E.D.: Handwritten digit recognition by combined classifiers. Kybern. Praha 34, 381–386 (1998)MATH
2.
3.
Feng, L., et al.: Spectral embedding-based multiview features fusion for content-based image retrieval. J. Electron. Imaging 26, 1 (2017)CrossRef
4.
Han, Y., Wu, F., Tao, D., Shao, J., Zhuang, Y., Jiang, J.: Sparse unsupervised dimensionality reduction for multiple view data. IEEE Trans. Circuits Syst. Video Technol. 22, 1485–1496 (2012)CrossRef
5.
Hou, C., Zhang, C., Wu, Y., Nie, F.: Multiple view semi-supervised dimensionality reduction. Pattern Recognit. 43, 720–730 (2010)CrossRef
6.
Huang, H., He, H., Fan, X., Zhang, J.: Super-resolution of human face image using canonical correlation analysis. Pattern Recognit. 43, 2532–2543 (2010)CrossRef
7.
Ji, H., Shen, X., Sun, Q., Ji, Z.: Sparse discrimination based multiset canonical correlation analysis for multi-feature fusion and recognition. In: British Machine Vision Conference, pp. 141.1–141.9 (2015)
8.
Juefei-Xu, F., Pal, D.K., Savvides, M.: NIR-VIS heterogeneous face recognition via cross-spectral joint dictionary learning and reconstruction. In: Computer Vision and Pattern Recognition Workshops, pp. 141–150 (2015)
9.
Kamada, C., Kanezaki, A., Harada, T.: Probabilistic semi-canonical correlation analysis. In: ACM International Conference on Multimedia, pp. 1131–1134 (2015)
10.
Kawashima, T., Ogawa, T., Haseyama, M.: A rating prediction method for e-commerce application using ordinal regression based on LDA with multi-modal features. In: Consumer Electronics, pp. 260–261 (2013)
11.
12.
13.
Lai, P.L., Fyfe, C.: Kernel and nonlinear canonical correlation analysis. Int. J. Neural Syst. 10, 365–377 (2000)CrossRef
14.
Lawrence, C.T.: A computationally efficient feasible sequential quadratic programming algorithm. Soc. Ind. Appl. Math. 11, 1092–1118 (2000)MathSciNetMATH
15.
Melzer, T., Reiter, M., Bischof, H.: Appearance models based on kernel canonical correlation analysis. Pattern Recognit. 36, 1961–1971 (2003)CrossRef
16.
Meng, R., Rice, S.G., Wang, J., Sun, X.: A fusion steganographic algorithm based on faster R-CNN. Comput. Mater. Contin. 55, 1–16 (2018)
17.
18.
Pearson, E.S.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)CrossRef
19.
20.
Rupnik, J., Grobelnik, M.: Cross-lingual search over 22 european languages. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 883–883 (2008)
21.
Sargin, M.E., Erzin, E., Yemez, Y., Tekalp, A.M.: Multimodal speaker identification using canonical correlation analysis. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, pp. 613–616 (2006)
22.
Sargin, M.E., Yemez, Y., Erzin, E., Tekalp, A.M.: Audiovisual synchronization and fusion using canonical correlation analysis. IEEE Trans. Multimed. 9, 1396–1403 (2007)CrossRef
23.
Sun, B.Y., Li, J., Wu, D.D., Zhang, X.M., Li, W.B.: Kernel discriminant learning for ordinal regression. IEEE Trans. Knowl. Data Eng. 22, 906–910 (2009)CrossRef
24.
Sun, T., Chen, S., Yang, J., Shi, P.: A novel method of combined feature extraction for recognition. In: Eighth IEEE International Conference on Data Mining, pp. 1043–1048 (2008a)
25.
Sun, T.K., Chen, S.C., Jin, Z., Yang, J.Y.: Kernelized discriminative canonical correlation analysis. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 1283–1287 (2008b)
26.
Tae-Kyun, K., Roberto, C.: Canonical correlation analysis of video volume tensors for action categorization and detection. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1415–1428 (2009)CrossRef
27.
Tian, Q., Chen, S.: Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238, 286–295 (2017)CrossRef
28.
Vinokourov, A., Shawe-Taylor, J., Cristianini, N.: Inferring a semantic representation of text via cross-language correlation analysis. In: Advances of Neural Information Processing Systems, pp. 1497–1504 (2002)
29.
Wang, L., Chen, S.: Joint representation classification for collective face recognition. Pattern Recognit. 63, 182–192 (2017)CrossRef
30.
Zeng, D., Dai, Y., Li, F., Sherratt, R.S., Wang, J.: Adversarial learning for distant supervised relation extraction. Comput. Mater. Contin. 55, 1–16 (2018)
31.
Zhou, H.X., Chen, S.C.: Ordinal discriminative canonical correlation analysis. J. Softw. 25, 2018–2025 (2014)MATH
Metadaten
Titel
Robust Manifold Learning Based Ordinal Discriminative Correlation Regression
verfasst von
Qing Tian
Wenqiang Zhang
Liping Wang
Copyright-Jahr
2018
Buch
Cloud Computing and Security

Print ISBN: 978-3-030-00020-2

Electronic ISBN: 978-3-030-00021-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-00021-9_60