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We evaluate our new multiview learning framework on four broadly used benchmark data sets. Each data set has a certain number of types of features (views), summarized in Table 1.

Table 1:
Details of Data Sets and Multiview Features Used in the Experiments.
Feature IDNUS-WIDE-ObjectOutdoor SceneMSRC-v1Handwritten Digit
Color histogram (64-D) GIST (512-D) Color Moment (48-D) FOU(76-D) 
Color correlogram (144-D) Color Moment (432-D) LBP (256-D) FAC(216-D) 
Edge direction histogram (73-D) HOG (256-D) HOG (100-D) KAR (64-D) 
Wavelet texture (128-D) LBP (48-D) SIFT (1230-D) PIX (240-D) 
Block-wise color moments (225-D) GIST (512-D) ZER (47-D) 
BoW SIFT (500-D) CENTRIST (1320-D) 
Number of classes 31 10 
Size 30,000 2688 210 2000 
Feature IDNUS-WIDE-ObjectOutdoor SceneMSRC-v1Handwritten Digit
Color histogram (64-D) GIST (512-D) Color Moment (48-D) FOU(76-D) 
Color correlogram (144-D) Color Moment (432-D) LBP (256-D) FAC(216-D) 
Edge direction histogram (73-D) HOG (256-D) HOG (100-D) KAR (64-D) 
Wavelet texture (128-D) LBP (48-D) SIFT (1230-D) PIX (240-D) 
Block-wise color moments (225-D) GIST (512-D) ZER (47-D) 
BoW SIFT (500-D) CENTRIST (1320-D) 
Number of classes 31 10 
Size 30,000 2688 210 2000 

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