A crucial issue in heterogeneous domain adaptation hda is the ability to learn a feature mapping between different types of features across domains. In this paper we present an efficient multi class heterogeneous domain adaptation method where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically we propose to recon struct a sparse feature transformation matrix to map the weight vector of.
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