Advanced search
1 file | 1.36 MB Add to list

Kernel-based distance metric learning for supervised k-means clustering

Bac Nguyen Cong (UGent) and Bernard De Baets (UGent)
Author
Organization
Abstract
Finding an appropriate distance metric that accurately reflects the (dis)similarity between examples is a key to the success of k-means clustering. While it is not always an easy task to specify a good distance metric, we can try to learn one based on prior knowledge from some available clustered data sets, an approach that is referred to as supervised clustering. In this paper, a kernel-based distance metric learning method is developed to improve the practical use of k-means clustering. Given the corresponding optimization problem, we derive a meaningful Lagrange dual formulation and introduce an efficient algorithm in order to reduce the training complexity. Our formulation is simple to implement, allowing a large-scale distance metric learning problem to be solved in a computationally tractable way. Experimental results show that the proposed method yields more robust and better performances on synthetic as well as real-world data sets compared to other state-of-the-art distance metric learning methods.
Keywords
Kernel learning, k-means clustering, metric learning, supervised clustering, FRAMEWORK, SIMULATION

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.36 MB

Citation

Please use this url to cite or link to this publication:

MLA
Nguyen Cong, Bac, and Bernard De Baets. “Kernel-Based Distance Metric Learning for Supervised k-Means Clustering.” IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 30, no. 10, 2019, pp. 3084–95.
APA
Nguyen Cong, B., & De Baets, B. (2019). Kernel-based distance metric learning for supervised k-means clustering. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 30(10), 3084–3095.
Chicago author-date
Nguyen Cong, Bac, and Bernard De Baets. 2019. “Kernel-Based Distance Metric Learning for Supervised k-Means Clustering.” IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30 (10): 3084–95.
Chicago author-date (all authors)
Nguyen Cong, Bac, and Bernard De Baets. 2019. “Kernel-Based Distance Metric Learning for Supervised k-Means Clustering.” IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30 (10): 3084–3095.
Vancouver
1.
Nguyen Cong B, De Baets B. Kernel-based distance metric learning for supervised k-means clustering. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. 2019;30(10):3084–95.
IEEE
[1]
B. Nguyen Cong and B. De Baets, “Kernel-based distance metric learning for supervised k-means clustering,” IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 30, no. 10, pp. 3084–3095, 2019.
@article{8589333,
  abstract     = {Finding an appropriate distance metric that accurately reflects the (dis)similarity between examples is a key to the success of k-means clustering. While it is not always an easy task to specify a good distance metric, we can try to learn one based on prior knowledge from some available clustered data sets, an approach that is referred to as supervised clustering. In this paper, a kernel-based distance metric learning method is developed to improve the practical use of k-means clustering. Given the corresponding optimization problem, we derive a meaningful Lagrange dual formulation and introduce an efficient algorithm in order to reduce the training complexity. Our formulation is simple to implement, allowing a large-scale distance metric learning problem to be solved in a computationally tractable way. Experimental results show that the proposed method yields more robust and better performances on synthetic as well as real-world data sets compared to other state-of-the-art distance metric learning methods.},
  author       = {Nguyen Cong, Bac and De Baets, Bernard},
  issn         = {2162-237X},
  journal      = {IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS},
  keywords     = {Kernel learning,k-means clustering,metric learning,supervised clustering,FRAMEWORK,SIMULATION},
  language     = {eng},
  number       = {10},
  pages        = {3084--3095},
  title        = {Kernel-based distance metric learning for supervised k-means clustering},
  url          = {http://dx.doi.org/10.1109/tnnls.2018.2890021},
  volume       = {30},
  year         = {2019},
}

Altmetric
View in Altmetric
Web of Science
Times cited: