Euclidean co-embedding of ordinal data for multi-type visualization

• Main Authors: LE, Dung D., LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Euclidean; High-dimensional; Information preservation; Low-dimensional representation; Objective metrics; Ordinal data; data visualization; data mining; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/3358; https://ink.library.smu.edu.sg/context/sis_research/article/4360/viewcontent/EuclideanCo_embedding.pdf
• Institution: Singapore Management University

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.