they are not consistently better than ranking models that do not rely on fusion.

This paper presents an unsupervised representation model, based on rank-fusion graphs, for general applicability in multimodal prediction tasks, such as classification and regression. We explore and extend the concept of a rank-fusion graph, that was originally proposed as part of a rank aggregation approach for retrieval tasks [@Dourado:2019:FG].

We present a fusion method based on the representation of multiple ranks, defined according to different criteria, into a graph. Graphs provide an efficient representation of arbitrary structures and inter-relationships among different elements of a model. We embed the generated graph into a feature space, creating fusion vectors. Next, an estimator is trained to predict if an input multimodal object refers to a target label (or event) or not, following their fusion vectors.

In [@Dourado:2019:FV], a methodology to apply rank-fusion graphs for efficient retrieval was presented, in the context of retrieval tasks. Here we explore those graph embedding approaches of rank-fusion graphs, now targeting a representation model for prediction tasks, either supervised or unsupervised. For this purpose, we propose specific components for the training and prediction phases, as well as a new application for the fusion vectors.

Our 