TESI DOCTORAL - La Salle

5.4. Discussion
Data set
Relative φ (NMI) difference
with respect to
BEC MEC
IsoLetters -17.5 53.2
CAL500 -34.7 13.2
InternetAds -65.2 138.7
Corel -28.9 169.1
Table 5.15: Relative φ (NMI) percentage differences between the best and median components
of the cluster ensemble and the consensus clustering λ final
c selected by supraconsensus,
for the four multimedia data collections, averaged across the seven consensus functions
employed.
In average, λ final
c
is, in relative percentage terms, a 36.6% worse than the BEC and a
93.5% better than the MEC. As expected, the price to pay for the lack of precision of the
supraconsensus function is a reduction of the quality of the final clustering solution.
5.4 Discussion
In this chapter, we have proposed and experimentally explored the use of consensus clustering
strategies for partitioning multimedia data collections robustly. From our viewpoint,
this application constitutes a natural extension of the computationally efficient consensus
architectures presented in chapter 3 and the self-refining procedures proposed in chapter
4. As mentioned earlier, the growing ubiquity of multimedia data makes the proposals put
forward in the present chapter even more appealing.
Across the experiments presented in this chapter and in appendix B.2, we have observed
that partitioning multimodal data sets in a robust manner is more tricky than doing so in a
unimodal scenario, as the existence of multiple modalities in the data increases the already
numerous indeterminacies inherent to the clustering problem.
As a means for fighting against this fact, modality fusion has become a recurrent issue in
the multimedia data analysis literature. Indeed, assuming that combining the distinct data
modalities can be of interest is pretty logical, as it is an obvious way to take advantage of
the expectably existing constructive dependences between them. In this sense, and focusing
on the clustering problem, there exist two main approaches to modality fusion: early (aka
feature level) fusion and late (classification decision) fusion.
However, our experiments have revealed that none of these fusion strategies is, by itself,
capable of ensuring robust clustering results: in some cases, feature level fusion gives rise to
the best clustering results, whereas, in other cases, it simply constitutes a trade-off between
modalities. For this reason, our multimodal self-refining consensus clustering architectures
constitute a generic approach for partitioning multimedia data collections with a reasonable
degree of robustness, with the advantage of encompassing, simultaneously, early and late
fusion techniques.
To our knowledge, most works dealing with multimodal clustering focus on feature level
fusion, deriving novel early fusion approaches for combining modalities (see section 1.4
for a brief review). However, they often disregard the fact that, in some data sets, early
160