IEEE Trans Pattern Anal Mach Intell - Learning with Augmented Features for Supervised and Semi-supervised Heterogeneous Domain Adaptation.

Tópicos

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Resumo

In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then develop two new feature mapping functions for two domains, which respectively augments the transformed source and target samples with their original features and padding zeros. Existing supervised learning methods (e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods.

Resumo Limpo

paper studi heterogen domain adapt hda problem data sourc domain target domain repres heterogen featur differ dimens introduc two differ project matric first transform data two domain common subspac similar sampl across differ domain can measur develop two new featur map function two domain respect augment transform sourc target sampl origin featur pad zero exist supervis learn method eg svm svr can readili employ incorpor newli propos augment featur represent supervis hda showcas propos novel method call heterogen featur augment hfa base svm show propos formul can equival deriv standard multipl kernel learn mkl problem convex thus global solut can guarante addit util unlabel data target domain propos semisupervis hfa shfa can simultan learn target classifi well infer label unlabel target sampl comprehens experi three differ applic clear demonstr shfa hfa outperform exist hda method

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