Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2016
Abstract
Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.
Keywords
Euclidean, High-dimensional, Information preservation, Low-dimensional representation, Objective metrics, Ordinal data, data visualization, data mining
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, May 5-7
First Page
396
Last Page
404
ISBN
9781611974348
Identifier
10.1137/1.9781611974348.45
Publisher
SIAM
City or Country
Philadelphia, PA
Citation
LE, Dung D. and LAUW, Hady W..
Euclidean co-embedding of ordinal data for multi-type visualization. (2016). Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, May 5-7. 396-404.
Available at: https://ink.library.smu.edu.sg/sis_research/3358
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1137/1.9781611974348.45
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons