TESI DOCTORAL - La Salle

Appendix B. Experiments on clustering indeterminacies
Data representation
CAL500
Data set
Corel InternetAds IsoLetters
MM 28 28 28 28
Baseline M1 28 28 28 28
M2 28 28 28 28
MM 504 420 308 532
PCA M1 280 196 308 196
M2 140 224 392 532
MM 504 420 308 532
ICA M1 280 196 308 196
M2 140 224 392 532
MM – 420 – 532
NMF M1 – 196 – 196
M2 – 224 – 532
MM 504 420 308 532
RP M1 280 196 308 196
M2 140 224 392 532
Table B.3: Number of individual clusterings per data representation on each multimodal
data set, where MM, M1 and M2 stand for multimodal, mode #1 and mode #2, respectively.
B.2.1 CAL500 data set
The φ (NMI) histograms presented in figure B.13 summarize the clustering results obtained by
running the aforementioned twenty-eight algorithms on each type of object representation
for each one of the two modalities, and for the multimodal representations as well.
If the histograms are compared representationwise, we observe that all representations
yield clustering solutions whose quality spans over similarly wide ranges below φ (NMI) =0.5.
For a given modality, there exists no clearly superior object representation.
However, if the histograms are compared across the modalities, it can be observed that
better results are obtained when clustering is conducted on the audio modality of this data
set, regardless of the type of representation employed. Moreover, the multimodal data
representation seems to yield intermediate quality clustering results (i.e. slightly better
than clustering on text only, but worse than clustering solely on audio), which reveals that
the early fusion of acoustic and textual features is not beneficial in this case.
B.2.2 Corel data set
Figure B.14 presents the φ (NMI) histograms corresponding to the multimodal and unimodal
clustering of the captioned images of the Corel data set.
As regards the comparison across object representations, it can be observed that, specially
for the image and multimodal modalities, the RP representation offers a large amount
of good clustering solutions, whereas the quality of the clusterings obtained on the remaining
representations is scattered over a wide range of φ (NMI) values.
If the clustering results obtained on the two modalities are compared, we can see that
the image modality is the one yielding the best clustering results (up to φ (NMI) =0.68),
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