Fig 1. The idea behind semi-supervised manifold alignment.

(A) Consider two data sources (red and black small points) in a binary problem (labeled points in orange balls and blue squares). SSMA aligns the dataset by (B) preserving their inner geometry and (C) registering the data clouds in the feature space using labels. (D) After alignment the datasets live in a semantically meaningful space.