Discriminative Factor Alignment across Heterogeneous Feature Space

Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from related domains for learning. While most of the existing works in this area are only focused on using the source data with the same representational structure as the target data, in this paper, we push this boundary further by extending transfer between text and images.

We integrate documents , tags and images to build a heterogeneous transfer learning factor alignment model and apply it to improve the performance of tag recommendation. Many algorithms for tag recommendation have been proposed, but many of them have problem; the algorithm may not perform well under cold start conditions or for items from the long tail of the tag frequency distribution. However, with the help of documents, our algorithm handles these problems and generally outperforms other tag recommendation methods, especially the non-transfer factor alignment model.