This paper presents a new and efficient approach for metric learning in latent space. Our method discovers an optimal mapping from the feature space to a latent space that shrinks the distance between similar data items and also increases...

constrained metric learning problem that can be solved using the Bregman׳s iterative projections. The proposed CS2GS is studied via a series of thorough tests using synthetic and real data including selections from UCI datasets and FEP – a recent bilingual...

in computing the similarity between new instances, unlike

local metric learning methods. (2) Automatically identifying the real dimension of latent subspaces, by defining

appropriate priors over the parameters of the system via a Bayesian...