We present a supervised dimensionality reduction technique suitable for visualizing multi-label images on a 2-D space. This method extends the use of the well-known t-distributed stochastic embedding (t-SNE) algorithm to the case of multi-labels instances, where the concept of partial relevance plays an important role. Furthermore, it is applicable straightaway for weakly annotated data. We apply our approach to generate 2-D representations of Mayan glyph-blocks, which are groups of individual glyph-signs expressing full sentences. The resulting representations are used to place visual instances in a 2-D space with the purpose of providing a browsable catalog for further epigraphic studies, where nearby instances are similar both in semantic and visual terms. We evaluate the performance of our approach quantitatively by performing classification and retrieval experiments. Our results show that this approach obtains high performance in both of these tasks.