IEEE Trans Neural Netw Learn Syst - Generalized multiple kernel learning with data-dependent priors.

Tópicos

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Resumo

Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination with a noninformative prior over multiple views is equivalent to the formulation of MKL. Instead of using the noninformative prior, we introduce a novel data-dependent prior based on an ensemble of kernel predictors, which enhances the prediction performance of MKL by leveraging the merits of the classifier ensemble. With the proposed probabilistic framework of MKL, we propose a hierarchical Bayesian model to learn the proposed data-dependent prior and classification model simultaneously. The resultant problem is convex and other information (e.g., instances with either missing views or missing labels) can be seamlessly incorporated into the data-dependent priors. Furthermore, a variety of existing MKL models can be recovered under the proposed MKL framework and can be readily extended to incorporate these priors. Extensive experiments demonstrate the benefits of our proposed framework in supervised and semisupervised settings, as well as in tasks with partial correspondence among multiple views.

Resumo Limpo

multipl kernel learn mkl classifi ensembl two mainstream method solv learn problem set featuresview inform other featuresview within given set inconsist paper first present novel probabilist interpret mkl maximum entropi discrimin noninform prior multipl view equival formul mkl instead use noninform prior introduc novel datadepend prior base ensembl kernel predictor enhanc predict perform mkl leverag merit classifi ensembl propos probabilist framework mkl propos hierarch bayesian model learn propos datadepend prior classif model simultan result problem convex inform eg instanc either miss view miss label can seamless incorpor datadepend prior furthermor varieti exist mkl model can recov propos mkl framework can readili extend incorpor prior extens experi demonstr benefit propos framework supervis semisupervis set well task partial correspond among multipl view

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