![](bg7.png)
Table 5: Complexity measures computed for the differ-
ent decompositions of automobile, eucalyptus and machine
datasets.
D
i
Weights Complexity measures
automobile δ
i
F1 F3 L1 N1
D
1
0.0000 13.41 0.99 0.04 0.05
D
2
0.0142 13.04 0.46 0.29 0.15
D
3
0.2200 0.83 0.22 0.43 0.22
D
4
0.0538 2.93 0.26 0.58 0.28
D
5
0.1950 0.96 0.57 0.33 0.16
eucalyptus δ
i
F1 F3 L1 N1
D
1
0.0191 2.72 0.17 0.40 0.20
D
2
0.3745 1.48 0.21 0.51 0.24
D
3
0.1235 1.80 0.10 0.58 0.31
D
4
0.0144 2.72 0.30 0.34 0.22
machine δ
i
F1 F3 L1 N1
D
1
0.2172 0.66 0.57 0.20 0.17
D
2
0.0204 0.89 0.43 0.42 0.19
D
3
0.0075 0.82 0.39 0.61 0.23
D
4
0.0209 0.97 0.30 0.77 0.26
D
5
0.0044 1.01 0.22 0.74 0.23
D
6
0.0104 1.13 0.22 0.63 0.23
D
7
0.0024 1.45 0.37 0.50 0.12
D
8
0.0115 1.80 0.45 0.39 0.09
D
9
0.5043 2.04 0.80 0.25 0.05
the computational complexity of our method (as ker-
nel learning methods usually present a high computa-
tional cost in this sense) and try to alleviate it via the
Nymstr
¨
on method for approximating Gram matrices
(Drineas and Mahoney, 2005).
ACKNOWLEDGEMENTS
This work has been subsidized by the TIN2011-22794
project of the Spanish Ministerial Commission of Sci-
ence and Technology (MICYT), FEDER funds and
the P11-TIC-7508 project of the “Junta de Andaluc
´
ıa”
(Spain).
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