and discussed in a more depth way as well. Finally, we evaluate the influence of hyper-parameters and provide guidelines for their selection.

Related Work
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Representation learning models have been developed and advanced for the data modalities individually, such as image, text, video, and audio. However, their combined exploration for multimodal tasks is still an open issue. Multimodality may also impose even more challenges, depending on the scenario, such as translation between modalities, exploration of complementarity and redundancy, co-learning, and semantic alignment [@baltruvsaitis:2018:multimodal].

Some works have focused on multimodal events, which require modeling spatio-temporal characteristics of data [@tzelepis2016event]. @faria2016fusion proposed a time-series descriptor that generates recurrence plots for the series, coupled with a bio-inspired optimization of how to combine classifiers. In this paper, we focus on multimodal tasks that do not depend on temporal modeling as well as unsupervised models.

In order to achieve fusion capabilities, *early-fusion* approaches emphasize the generation of composite descriptions for samples, thus working at feature level. Conversely, *late-fusion* approaches perform a combination of techniques focused on a target problem, fusing at score or decision level. On a smaller scale, some papers propose hybrid solutions based on both approaches [@zadeh2017tensor; @lan2014multimedia]. As we propose a representation 