a train set is demanded to contain labeled samples.

A *ranker* is a tuple ${R}= ({\mathcal{D}},{\mathcal{C}})$, which is employed to compute a rank ${\tau}$ for $q$, denoted by ${{\tau}_{q}}$ to distinguish its corresponding query. A rank is a permutation of $S_L \subseteq S$, where $L \ll n$ in general, such that ${{\tau}_{q}}$ provides the most similar – or equivalently the least dissimilar – samples to $q$ from $S$, in order. $L$ is a cut-off parameter. A ranker establishes a ranking system, but different descriptors and comparators can compose rankers. Besides, descriptors are commonly complementary, as well as comparators. For $m$ rankers, $\{R_j\}_{j=1}^m$, used for query retrieval over a collection $S$, for every query $q$ we can obtain ${\mathcal{T}_{q}}= \{{{\tau}_{j}}\}_{j=1}^m$, from which a *rank aggregation* function $f$ produces a combined rank ${{\tau}_{q,f}}=f({\mathcal{T}_{q}})$, presumably more effective than the individual ranks ${{\tau}_{1}}$, ${{\tau}_{2}}$, $\dots$, ${{\tau}_{m}}$.

Representation and Prediction Based on Graph-Based Rank Fusion {#secMethod}
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Figure \[fig:fusionVectorEstimation\] presents an overview of our method – a multimodal representation and estimator based on rank-fusion graphs. The solution is composed of three main generic components, briefly described here and detailed in the following sections. The multimodal representation is completely unsupervised, thus able to be adopted in 