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

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

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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