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We present the performance comparison of different algorithms in Table 2. It can be seen that the proposed algorithm MVCL consistently outperforms the other compared algorithms for different applications.

Table 2:
Classification Results of Different Applications Measured by Mean Average Precision.
MethodNUS-WIDE-ObjectSceneMSRC-v1Handwritten Digit
SVM (type 1)     
SVM (type 2)     
SVM (type 3)     
SVM (type 4)     
SVM (type 5)    
SVM (type 6)   
SVM (all by concatenation)     
SVM MKL method (Sonnenburg et al., 2006)     
SVM MKL method (Lanckriet et al., 2004)     
SVM MKL method (Kloft et al., 2008)     
LSSVM MKL method (Ye et al., 2008)     
LSSVM MKL method (Suykens & Vandewalle, 1999)     
LSSVM MKL method (Yu et al., 2010)     
GP method (Kapoor et al., 2010)     
LPboost- (Gehler & Nowozin, 2009)     
LPboost-B (Gehler & Nowozin, 2009)     
Multiview CCA (Foster et al., 2008)     
Multirelational Classification (Guo & Viktor, 2008)     
MAPGG (Wang & Mahadevan, 2013)     
KSCA (Jing et al., 2014)     
MVCL (no shared subspace)     
MVCL (shared subspace)     
MethodNUS-WIDE-ObjectSceneMSRC-v1Handwritten Digit
SVM (type 1)     
SVM (type 2)     
SVM (type 3)     
SVM (type 4)     
SVM (type 5)    
SVM (type 6)   
SVM (all by concatenation)     
SVM MKL method (Sonnenburg et al., 2006)     
SVM MKL method (Lanckriet et al., 2004)     
SVM MKL method (Kloft et al., 2008)     
LSSVM MKL method (Ye et al., 2008)     
LSSVM MKL method (Suykens & Vandewalle, 1999)     
LSSVM MKL method (Yu et al., 2010)     
GP method (Kapoor et al., 2010)     
LPboost- (Gehler & Nowozin, 2009)     
LPboost-B (Gehler & Nowozin, 2009)     
Multiview CCA (Foster et al., 2008)     
Multirelational Classification (Guo & Viktor, 2008)     
MAPGG (Wang & Mahadevan, 2013)     
KSCA (Jing et al., 2014)     
MVCL (no shared subspace)     
MVCL (shared subspace)     

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