as they need both multimodal composition and supervision, they are tied to the domain of interest. Supervised early-fusion usually suffers from high memory and time consumption costs. Besides, they usually have difficulty in preserving feature-based similarities and semantic correlations [@lan2014multimedia].

Late-fusion approaches are particularly useful when the raw data from the objects are not available. Besides, they are less prone to over-fit. Mixture of experts (MoE) approaches focus on performing decision fusion, combining predictors to address a supervised learning problem [@yuksel2012twenty]. Majority voting of classifiers [@nogueira2018exploiting], rank aggregation functions [@Dourado:2019:FG] and matrix factorization [@dong2018late] are examples of late-fusion methods. Both fusions based on rank aggregation functions and matrix factorization are based on manifold learning, i.e. the exploration of dataset geometry.

Majority voting is a well-known approach to combine multiple estimators, being effective due to bias reduction. It is applied to scenarios involving an odd number of estimators, so that each predicted output is taken as the one most frequently predicted by the base estimators.

Rank aggregation functions combine results from different retrieval models. They target a good permutation of retrieved objects obtained from different input ranks, in order to promote more effective retrieval results [@Dourado:2019:FG].

In general, unsupervised learning needs 