PT Unknown
AU Albert Gordo
   Florent Perronnin
   Ernest Valveny
TI Document classification using multiple views
BT 10th IAPR International Workshop on Document Analysis Systems
PY 2012
BP 33
EP 37
DI 10.1109/DAS.2012.30
AB The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.
ER