TESI DOCTORAL - La Salle
TESI DOCTORAL - La Salle
TESI DOCTORAL - La Salle
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φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
text<br />
λ agglo−cos−upgma<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(a) Modality 1<br />
φ (NMI)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
Appendix E. Experiments on multimodal consensus clustering<br />
image<br />
λ agglo−cos−upgma<br />
c<br />
1<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(b) Modality 2<br />
φ (NMI)<br />
text+image<br />
λ agglo−cos−upgma<br />
c<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(c) Multimodal<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
λ c agglo−cos−upgma<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(d) Intermodal<br />
Figure E.17: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering<br />
solutions using the agglo-cos-upgma algorithm on the Corel data set.<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
text<br />
λ direct−cos−i2<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(a) Modality 1<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
image<br />
λ direct−cos−i2<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(b) Modality 2<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
text+image<br />
λ direct−cos−i2<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(c) Multimodal<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
λ c direct−cos−i2<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(d) Intermodal<br />
Figure E.18: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering<br />
solutions using the direct-cos-i2 algorithm on the Corel data set.<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
text<br />
λ graph−cos−i2<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(a) Modality 1<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
image<br />
λ graph−cos−i2<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(b) Modality 2<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
text+image<br />
λ graph−cos−i2<br />
c<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(c) Multimodal<br />
φ (NMI)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
λ c graph−cos−i2<br />
E<br />
CSPA<br />
EAC<br />
HGPA<br />
MCLA<br />
ALSAD<br />
KMSAD<br />
SLSAD<br />
(d) Intermodal<br />
Figure E.19: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering<br />
solutions using the graph-cos-i2 algorithm on the Corel data set.<br />
E.3.2 Self-refined consensus clustering across modalities<br />
In the following paragraphs, the results of applying the consensus self-refining procedure on<br />
the intermodal consensus clustering λc are qualitatively described.<br />
For starters, the φ (NMI) values of the non-refined and self-refined consensus clusterings<br />
obtained on the agglo-cos-upgma are presented in figure E.21. We can observe that, in<br />
all cases, there exists at least one refined consensus clustering that attains a higher φ (NMI)<br />
value than the non-refined clustering λc. Moreover, in this case, the supraconsensus function<br />
is pretty successful in selecting the top quality consensus clustering as the final partition<br />
369
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