A bayesian framework for learning shared and individual subspaces from multiple data sources
MetadataShow full item record
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds.
Showing items related by title, author, creator and subject.
Gupta, Sunil Kumar (2011)The growing number of information sources has given rise to joint analysis. While the research community has mainly focused on analyzing data from a single source, there has been relatively few attempts on jointly analyzing ...
Lisk, Mark (2012)A comprehensive examination of the hydrocarbon charge and formation water history of the central Vulcan Sub-basin, Timor Sea has been completed and a model developed to describe the evolution of the region’s petroleum ...
Spatio-temporal geochemical evolution of the SE Australian upper mantle deciphered from the Sr, Nd and Pb isotope compositions of Cenozoic intraplate volcanic rocksOostingh, K.; Jourdan, Fred; Merle, R.; Chiaradia, M. (2016)Intraplate basaltic volcanic rocks ranging in age from Late Cretaceous to Holocene are distributed across southeastern Australia in Victoria and eastern South Australia. They comprise four provinces differentiated on the ...