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:
Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Transferable Deep Metric Learning for Clustering

Authors : Mohamed Alami Chehboune, Rim Kaddah, Jesse Read

Published in: Advances in Intelligent Data Analysis XXI

Publisher: Springer Nature Switzerland

Log in

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

search-config
loading …

Abstract

Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric is crucial, and it is highly dependent on the dataset characteristics. However a single metric could be used to correctly perform clustering on multiple datasets of different domains. We propose to do so, providing a framework for learning a transferable metric. We show that we can learn a metric on a labelled dataset, then apply it to cluster a different dataset, using an embedding space that characterises a desired clustering in the generic sense. We learn and test such metrics on several datasets of variable complexity (synthetic, MNIST, SVHN, omniglot) and achieve results competitive with the state-of-the-art while using only a small number of labelled training datasets and shallow networks.

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 Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study
next chapter Spatial Graph Convolution Neural Networks for Water Distribution Systems
Footnotes
1
As a reminder, Let T and U be two topological spaces. A function \(f:T\mapsto U\) is continuous in the open set definition if for every \(t\in T\) and every open set u containing f(t), there exists a neighbourhood v of t such that \(f(v)\subset u\).
 
Literature
1.
Arjovsky, M., et al.: Wasserstein generative adversarial networks. In: ICML, pp. 214–223 (2017)
2.
Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: ICML, p. 11 (2004)
3.
4.
Cohen, G., et al.: EMNIST: Extending MNIST to handwritten letters. In: IJCNN (2017)
5.
Ganin, Y., et al.: Domain-adversarial training of neural networks. JMLR 17, 2096–2130 (2016)MathSciNet
6.
Han, K., et al.: Learning to discover novel visual categories via deep transfer clustering (2019)
7.
Han, K., Rebuffi, S.A., et al.: AutoNovel: automatically discovering and learning novel visual categories. PAMI 1 (2021)
8.
Hsu, et al.: Learning to cluster in order to transfer across domains and tasks (2017)
9.
Kipf, T.N., Welling, M.: Variational graph auto-encoders (2016)
10.
Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)MathSciNetCrossRefMATH
11.
LeCun, Y., Cortes, C., Burges, C.: MNIST handwritten digit database (2010)
12.
Netzer, Y., et al.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)
13.
Ng, A.Y., Russell, S.: Algorithms for inverse reinforcement learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 663–670 (2000)
14.
Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. ICML, pp. 2988–2997 (2017)
15.
Sener, O., Song, H.O., et al.: Learning transfer able representations for unsupervised domain adaptation. In: NIPS, pp. 2110–2118 (2016)
16.
Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. JMLR 3(Dec), 583–617 (2002)MathSciNetMATH
17.
Wang, X., Qian, B., Davidson, I.: On constrained spectral clustering and its applications. Data Min. Knowl. Discov. 28, 1–30 (2014)MathSciNetCrossRefMATH
18.
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (2017)
19.
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: ICML, pp. 478–487 (2016)
20.
Yang, Y., Xu, D., et al.: Image clustering using local discriminant models and global integration. IEEE Trans. Image Process. 19(10), 2761–2773 (2010)MathSciNetCrossRefMATH
Metadata
Title
Transferable Deep Metric Learning for Clustering
Authors
Mohamed Alami Chehboune
Rim Kaddah
Jesse Read
Copyright Year
2023
Book
Advances in Intelligent Data Analysis XXI

Print ISBN: 978-3-031-30046-2

Electronic ISBN: 978-3-031-30047-9

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-30047-9_2

Premium Partner

    Image Credits