We address the tasks of image classification and tagging through a transductive approach that automatically learns to project the different images onto a common latent space. This learned representation is then used to classify the images. We construst a graph between images and concepts using the deep representations given by ImageCLEF and WordNet database, and we exploit the idea that two connected nodes will tend to have a similar latent representation. This assumption allows us to learn correlations between the labels of connected images.