Multiview triplet embedding: Learning attributes in multiple maps

Ehsan Amid, Antti Ukkonen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    35 Citations (Scopus)


    For humans, it is usually easier to make statements about the similarity of objects in relative, rather than absolute terms. Moreover, subjective comparisons of objects can be based on a number of different and independent attributes. For example, objects can be compared based on their shape, color, etc. In this paper, we consider the problem of uncovering these hidden attributes given a set of relative distance judgments in the form of triplets. The attribute that was used to generate a particular triplet in this set is unknown. Such data occurs, e.g., in crowdsourcing applications where the triplets are collected from a large group of workers. We propose the Multiview Triplet Embedding (MVTE) algorithm that produces a number of low-dimensional maps, each corresponding to one of the hidden attributes. The method can be used to assess how many different attributes were used to create the triplets, as well as to assess the difficulty of a distance comparison task, and find objects that have multiple interpretations in relation to the other objects.

    Original languageEnglish
    Title of host publication32nd International Conference on Machine Learning, ICML 2015
    Number of pages9
    ISBN (Electronic)9781510810587
    Publication statusPublished - 2015
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Machine Learning - Lille, France
    Duration: 6 Jul 201511 Jul 2015
    Conference number: 32


    ConferenceInternational Conference on Machine Learning
    Abbreviated titleICML


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