Neural Comput - Information-theoretic semi-supervised metric learning via entropy regularization.

Tópicos

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Resumo

We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For metric learning, entropy regularization improves manifold regularization by considering the dissimilarity information of unlabeled data in the unsupervised part, and hence it allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Moreover, we regularize SERAPH by trace-norm regularization to encourage low-dimensional projections associated with the distance metric. The nonconvex optimization problem of SERAPH could be solved efficiently and stably by either a gradient projection algorithm or an EM-like iterative algorithm whose M-step is convex. Experiments demonstrate that SERAPH compares favorably with many well-known metric learning methods, and the learned Mahalanobis distance possesses high discriminability even under noisy environments.

Resumo Limpo

propos general informationtheoret approach semisupervis metric learn call seraph semisupervis metric learn paradigm hyperspars reli manifold assumpt given probabl parameter mahalanobi distanc maxim entropi label data minim entropi unlabel data follow entropi regular metric learn entropi regular improv manifold regular consid dissimilar inform unlabel data unsupervis part henc allow supervis unsupervis part integr natur meaning way moreov regular seraph tracenorm regular encourag lowdimension project associ distanc metric nonconvex optim problem seraph solv effici stabli either gradient project algorithm emlik iter algorithm whose mstep convex experi demonstr seraph compar favor mani wellknown metric learn method learn mahalanobi distanc possess high discrimin even noisi environ

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