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Found 6 papers, 0 papers with code
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
However, there does not exist a fair and thorough assessment of these embedding methods and therefore several key questions remain unanswered: Which algorithms perform better when the embedding dimension is constrained or few triplet comparisons are available?
LARGE SCALE REPRESENTATION LEARNING FROM TRIPLET COMPARISONS
This problem has been studied in a sub-community of machine learning by the name "Ordinal Embedding".
Estimation of perceptual scales using ordinal embedding
We propose to use ordinal embedding methods from machine learning to estimate the scaling function from the relative judgments.
Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing
In this paper we study whether the use of comparison-based (ordinal) data, combined with machine learning algorithms, can boost the reliability of crowdsourcing studies for psychophysics, such that they can achieve performance close to a lab experiment.
Comparison-Based Random Forests
Assume we are given a set of items from a general metric space, but we neither have access to the representation of the data nor to the distances between data points.
Comparison Based Nearest Neighbor Search
We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points.
