IEEE Trans Pattern Anal Mach Intell - Multi-Exemplar Affinity Propagation.

Tópicos

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Resumo

Affinity Propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multi-subclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new Multi-Exemplar Affinity Propagation (MEAP) algorithm. This new model determines automatically the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and super-exemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

Resumo Limpo

affin propag ap cluster algorithm receiv much attent past year ap appeal effici insensit initi produc cluster lower error rate exemplarbas method howev singleexemplar model becom inadequ appli model multisubclass situat scene analysi charact recognit remedi defici extend singleexemplar model multiexemplar one creat new multiexemplar affin propag meap algorithm new model determin automat number exemplar cluster associ super exemplar approxim subclass categori solv model nphard tackl maxsum belief propag produc neighborhood maximum cluster need specifi beforehand number cluster multiexemplar superexemplar also util sparsiti data abl reduc substanti comput time storag experiment studi shown meap signific improv algorithm unsupervis imag categor cluster handwritten digit

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