Loading...

Learning a metric when clustering data points in the presence of constraints

Abin, A. A ; Sharif University of Technology | 2020

483 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/s11634-019-00359-6
  3. Publisher: Springer , 2020
  4. Abstract:
  5. Learning an appropriate distance measure under supervision of side information has become a topic of significant interest within machine learning community. In this paper, we address the problem of metric learning for constrained clustering by considering three important issues: (1) considering importance degree for constraints, (2) preserving the topological structure of data, and (3) preserving some natural distribution properties in the data. This work provides a unified way to handle different issues in constrained clustering by learning an appropriate distance measure. It has modeled the first issue by injecting the importance degree of constraints directly into an objective function. The topological structure of data is preserved by minimizing the reconstruction error of data in the target space. Finally we addressed the issue of preserving natural distribution properties in the data by using the proximity information of data. We have proposed two different methods to address the above mentioned issues. The first approach learns a linear transformation of data into a target space (linear-model) and the second one uses kernel functions to learn an appropriate distance measure (non-linear-model). Experiments show that considering these issues significantly improves clustering accuracy. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature
  6. Keywords:
  7. Constrained clustering ; Instance-level constraints ; Metric learning ; Clustering algorithms ; Linear transformations ; Mathematical transformations ; Topology ; Appropriate distances ; Machine learning communities ; Natural distribution ; Topological structure ; Weighted constraints ; Metadata
  8. Source: Advances in Data Analysis and Classification ; Volume 14, Issue 1 , 2020 , Pages 29-56
  9. URL: https://link.springer.com/article/10.1007/s11634-019-00359-6