IEEE Trans Pattern Anal Mach Intell - Semi-Supervised Kernel Mean Shift Clustering.

Tópicos

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Resumo

Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function.We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms.

Resumo Limpo

mean shift cluster power nonparametr techniqu requir prior knowledg number cluster constrain shape cluster howev complet unsupervis perform suffer origin distanc metric fail captur under cluster structur despit recent advanc semisupervis cluster method littl effort toward incorpor supervis mean shift propos semisupervis framework kernel mean shift cluster skms use pairwis constraint guid cluster procedur point first map highdimension kernel space constraint impos linear transform map point achiev modifi initi kernel matrix minim log det divergencebas object functionw show advantag skms evalu perform various synthet real dataset compar stateoftheart semisupervis cluster algorithm

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