  Recent years, transfer learning has attracted much attention in the community
of machine learning. In this paper, we mainly focus on the tasks of parameter
transfer under the framework of extreme learning machine (ELM). Unlike the
existing parameter transfer approaches, which incorporate the source model
information into the target by regularizing the di erence between the source
and target domain parameters, an intuitively appealing projective-model is
proposed to bridge the source and target model parameters. Specifically, we
formulate the parameter transfer in the ELM networks by the means of parameter
projection, and train the model by optimizing the projection matrix and
classifier parameters jointly. Further more, the `L2,1-norm structured sparsity
penalty is imposed on the source domain parameters, which encourages the joint
feature selection and parameter transfer. To evaluate the e ectiveness of the
proposed method, comprehensive experiments on several commonly used domain
adaptation datasets are presented. The results show that the proposed method
significantly outperforms the non-transfer ELM networks and other classical
transfer learning methods.
