TESI DOCTORAL - La Salle

clustering count
clustering count
clustering count
60
40
20
Corel Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
(multimodal)
60
40
20
Corel Baseline M1
0
0 0.5 1
φ (NMI)
(f) Baseline
(image)
60
40
20
Corel Baseline M2
0
0 0.5 1
φ (NMI)
(k) Baseline
(text)
clustering count
clustering count
clustering count
60
40
20
Corel PCA
0
0 0.5 1
φ (NMI)
(b) PCA (multimodal)
60
40
20
Corel PCA M1
0
0 0.5 1
φ (NMI)
(g) PCA (image)
60
40
20
Corel PCA M2
0
0 0.5 1
φ (NMI)
(l) PCA (text)
Appendix B. Experiments on clustering indeterminacies
clustering count
clustering count
clustering count
60
40
20
Corel ICA
0
0 0.5 1
φ (NMI)
(c) ICA (multimodal)
60
40
20
Corel ICA M1
0
0 0.5 1
φ (NMI)
(h) ICA (image)
60
40
20
Corel ICA M2
0
0 0.5 1
φ (NMI)
(m) ICA (text)
clustering count
clustering count
clustering count
60
40
20
Corel NMF
0
0 0.5 1
φ (NMI)
(d) NMF (multimodal)
60
40
20
Corel NMF M1
0
0 0.5 1
φ (NMI)
(i) NMF (image)
60
40
20
Corel NMF M2
0
0 0.5 1
φ (NMI)
(n) NMF (text)
clustering count
clustering count
clustering count
60
40
20
Corel RP
0
0 0.5 1
φ (NMI)
(e) RP (multimodal)
60
40
20
Corel RP M1
0
0 0.5 1
φ (NMI)
60
40
20
(j) RP (image)
Corel RP M2
0
0 0.5 1
φ (NMI)
(o) RP (text)
Figure B.14: Histograms of the φ (NMI) values on the Corel data set obtained on the following
data representations.
B.2.4 IsoLetters data set
The collection of clustering solutions obtained on the IsoLetters artificial multimodal data
collection are presented representation and modality-wise in figure B.16.
In this case, the quality of the clusterings created on the distinct object representations
present two clearly different histogram patterns depending on the modality. For instance,
in the speech modality (figures B.16(e) to B.16(h)), the baseline and RP histograms present
a main and a secondary minor peaks, whereas the PCA and ICA representations yield a
pretty uniform distribution of clusterings. In contrast, a totally different distribution is
found when clustering is run on the visual mode, where a single negatively skewed bell
shape is observed (see figures B.16(i) to B.16(l)).
Finally, it is to note that, regardless of the object representation employed, the early
fusion of the speech and visual features of this data set gives rise to a notable increase in
the quality of the clustering results (a 16.2% in averaged relative terms as regards the top
quality individual clustering solution).
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