Semi-supervised t-SNE with multi-scale neighborhood preservation

•A semi-supervised framework based on similarity preservation for dimensionality reduction is presented.•Class-label and data structure insights are coupled for neighbourhood preservation based on the well-known t-SNE approach.•Our SS. t-SNE is a well-founded generalization of the t-SNE method from multi-scale neighborhood preservation and class-label coupling within a divergence-based loss.•Visualization, rank, and classification performance criteria are tested on synthetic and real-world datasets devoted to dimensionality reduction and data discrimination.