University of Cambridge > Talks.cam > Machine Learning @ CUED > Unsupervised Many-to-many Object Matching

Unsupervised Many-to-many Object Matching

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Object matching is a task for finding correspondences between objects in different domains. Examples of object matching include matching an English word with a French word, and a user identification with a user identification in a different database. Most of the object matching methods are supervised; they require similarity measures or paired data. We propose a probabilistic latent variable model for unsupervised object matching, which can find many-to-many matching without alignment information. The proposed model assumes that there are an infinite number of latent vectors that are shared by all domains, and that each object is generated using one of the latent vectors and a domain-specific linear projection. By inferring a latent vector to be used for generating each object, objects in different domains are clustered in shared groups, and thus we can find matching between clusters in an unsupervised manner. We present efficient inference procedures for the proposed model based on a stochastic EM algorithm. The effectiveness of the proposed model is demonstrated with experiments using synthetic and real data sets.

This talk is part of the Machine Learning @ CUED series.

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