Armed with the pseudo-labelled data generated via Algorithm 1, we can now learn a projection for the target domain, T prj , following the same procedure we proposed for learning S prj in Section 3.1.
t prj is defined by the two target-domain projection matrices B + ∈ R d×d and B − ∈ R d×d that maximises the distance between each pseudo-labelled target instance x and its k opposite labelled nearest neighbours se-lected from positively (t + L ) and negatively (t − L ) pseudo-labelled instances.