Multiview Triplet Embedding: Learning Attributes in Multiple Maps

For example, objects can be compared based on their shape, color, etc.

 

In this talk, 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. Unlike existing MDS techniques that produce only one map, our 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.

 

 

Bio: Ehsan Amid received his M.Sc. in Machine Learning and Data Mining from Aalto University in 2014. He is currently a research assistant in the Data Mining group at Aalto University. He is working towards Ph.D. at the University of California, Santa Cruz. His research interests include dimensionality reduction, clustering, semi-supervised learning, and human computation.