Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

Ordinal embedding seeks a low-dimensional representation of objects based on relative comparisons of their similarities. This low-dimensional representation lends itself to visualization on a Euclidean map. Classical assumptions admit only one valid aspect of similarity. However, there are increasing scenarios involving ordinal comparisons that inherently reflect multiple aspects of similarity, which would be better represented by multiple maps. We formulate this problem as conditional ordinal embedding, which learns a distinct low-dimensional representation conditioned on each aspect, yet allows collaboration across aspects via a shared representation. Our geometric approach is novel in its use of a shared spherical representation and multiple aspect-specific projection maps on tangent hyperplanes. Experiments on public datasets showcase the utility of collaborative learning over baselines that learn multiple maps independently.

Keywords

multiple maps, ordinal triplets, embedding

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 28th International Joint Conference on Artificial Intelligence: Macau, China, 2019 August 10-16

First Page

2815

Last Page

2822

Identifier

10.24963/ijcai.2019/390

Publisher

AAAI Press

City or Country

Menlo Park, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2019/390

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