Document Classification using Multiple Views
Albert Gordo, Florent Perronnin, Ernest Valveny
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.
10th IAPR International Workshop on Document Analysis Systems, Gold Coast, Queensland, Australia, March 27th-29th, 2012.