Fig. 1.

Illustration of the proposed method with K source domains and a target domain. Each source domain X_Si and the target domain X_T include samples from two categories (marked as triangles and circles). The first strategy of the proposed method is to transform each source domain and target domain into a latent representation domain via the specific projection P_i and the common projection P (with P_i = P + E_Pi, E_Pi is the sparse error term), respectively. Based on the transformed target domain data PX_T, the second strategy is to linearly represent each sample from each source domain using all samples from the target domain in the latent space (e.g., P_iX_Si = PX_TZ_Si + E_Si). Dotted arrows represent the first strategy, while the solid arrow represents the second strategy.