method has the advantage to be unsupervised, it also explores and captures relationships from the collection into the representation model, and it works on top of any descriptors for multimodal data such as visual or textual. By promoting a representation model solely based on base descriptors and unsupervised data analysis over the collection, we conjecture that our approach leads to a competitive multimodal representation model that explores and encodes information from multiple descriptors and underlying sample relationships automatically, while not requiring labeled data.

Experimental results over multiple multimodal and visual datasets demonstrate that the proposal is robust for different detection scenarios involving textual, visual, and multimodal features, yielding better detection results than state-of-the-art methods from both early-fusion and late-fusion approaches.

This paper extends the work presented in [@Dourado:2019:fusionGraphPrediction] where we introduced the notion of a representation model from rank-fusion graphs, and demonstrated its application for multimedia flood detection. Here we extend this formulation, proposing and discussing alternative approaches for the representation model. We also evaluate the method extensively over multiple multimodal and image scenarios, to analyze its applicability for prediction tasks in general. We also evaluate the method against additional baselines, either early-fusion and late-fusion approaches. Related work is presented 