<  Back to the Polytechnique Montréal portal

A Lagrangian-based approach to learn distance metrics for clustering with minimal data transformation

Rodrigo Randel, Daniel Aloise and Alain Hertz

Paper (2023)

An external link is available for this item
Department: Department of Computer Engineering and Software Engineering
Department of Mathematics and Industrial Engineering
PolyPublie URL: https://publications.polymtl.ca/57230/
Conference Title: SIAM International Conference on Data Mining (SDM 2023)
Conference Location: Minneapolis, MN, USA
Conference Date(s): 2023-04-27 - 2023-04-29
Publisher: Society for Industrial and Applied Mathematics Publications
DOI: 10.1137/1.9781611977653
Official URL: https://doi.org/10.1137/1.9781611977653
Date Deposited: 29 Jan 2024 14:38
Last Modified: 05 Apr 2024 12:05
Cite in APA 7: Randel, R., Aloise, D., & Hertz, A. (2023, April). A Lagrangian-based approach to learn distance metrics for clustering with minimal data transformation [Paper]. SIAM International Conference on Data Mining (SDM 2023), Minneapolis, MN, USA. https://doi.org/10.1137/1.9781611977653

Statistics

Dimensions

Repository Staff Only

View Item View Item