Class-dependent Canonical Correlation Analysis for scalable cross-lingual document categorization.

Canonical Correlation Analysis (CCA) is used to infer a semantic space into which text documents, written in different languages, can be mapped to a language-independent representation, called latent topics. This highly reduces the complexity of dealing with different languages since we can train a document classifier using the labeled documents in one language, and then apply it to classify documents in another language. This topic modeling task is usually performed in a class-independent manner. The performance of CCA depends on the amount of documents used for inferring the semantic space. However, CCA has a high computational complexity with respect to the number of training documents. In this paper, we proposed a scalable variant of CCA, CD-CCA, to improve its scalability and complexity where the projection is performed in a class-dependent manner. It generates a semantic space for each category separately. Then a binary document classifier is trained for each category on its own semantic space. CD-CCA was applied on English-Chinese document classification. The experimental results showed that CD-CCA can deal with large training sets without hurting the performance of the underlying classifiers compared to traditional CCA. CD-CCA opens the door for distributed training of the semantic spaces of the different categories.