Domain adaptive learning based on equilibrium distribution and dynamic subspace approximation

Nowadays, big data analysis has become an important approach in social information network. However, the social information may not be distributed independently and identically (i.i.d.), which can be addressed using domain adaptation. However, most of the existing domain adaptation methods are designed to align cross-domain distributions. The label information of the samples in the target domain is completely unavailable. Thus, the class-conditional distribution differences cannot be well measured, and the effect of feature distortion on distribution alignment in the original feature space is difficult to handle. This paper proposes a domain adaptation learning based on the Equilibrium Distribution and Dynamic Subspace Approximation (EDDSA) to alleviate these problems. First, EDDSA learns to project the source and target domains into associated feature spaces, and dynamically approximates two subspaces to overcome the feature distortion problem. Second, the balanced distribution alignment term is introduced to dynamically weight the importance of the conditional and marginal distributions. Through many experiments, EDDSA is superior to most traditional methods.