Paper: Heterogeneous Transfer Learning for Image Clustering via the SocialWeb

ACL ID P09-1001
Title Heterogeneous Transfer Learning for Image Clustering via the SocialWeb
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

In this paper, we present a new learning scenario, heterogeneous transfer learn- ing, which improves learning performance when the data can be in different feature spaces and where no correspondence be- tween data instances in these spaces is pro- vided. In the past, we have classified Chi- nese text documents using English train- ing data under the heterogeneous trans- fer learning framework. In this paper, we present image clustering as an exam- ple to illustrate how unsupervised learning can be improved by transferring knowl- edge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense dis- ambiguation in query-based image search, but its quality is often low due to image- data sparsity problem. We extend PLSA to help transfer the knowl...