Title: Completing missing views for multiple sources of web media

Authors: Shankara Subramanya, Zheshen Wang, Baoxin Li, Huan Liu

Addresses: Department of Computer Science and Engineering, Arizona State University, Tempe, AZ-85287, USA. ' Department of Computer Science and Engineering, Arizona State University, Tempe, AZ-85287, USA. ' Department of Computer Science and Engineering, Arizona State University, Tempe, AZ-85287, USA. ' Department of Computer Science and Engineering, Arizona State University, Tempe, AZ-85287, USA

Abstract: Combining multiple data sources, each with its own features, to achieve optimal inference has received a lot of attention in recent years. In inference from multiple data sources, each source can be thought of as providing one view of the underlying object. In general, different views may provide complementary information for the inference task. However, often not all the views are available all the time for the available instances in an application. In this paper, we propose a view completion approach based on canonical correlation analysis that heuristically predicts the missing views and further ranks all within-view features, through learning the intrinsic correlation among the views from training set. We evaluate our approach and compare it with existing approaches in the literature, using web page classification and photo tag recommendation as case studies. Experiments demonstrate the improved performance of the proposed approach. The results suggest that the work has great potential for inference problems with multiple information sources.

Keywords: canonical correlation analysis; CCA; view completion; feature selection; multiple data sources; optimal inference; web media; web page classification; photo tag recommendation; multiple information sources.

DOI: 10.1504/IJDMMM.2008.022536

International Journal of Data Mining, Modelling and Management, 2008 Vol.1 No.1, pp.23 - 44

Published online: 14 Jan 2009 *

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