A bayesian framework for learning shared and individual subspaces from multiple data sources

Authors:
Sunil Kumar Gupta;Dinh Phung;Brett Adams;Svetha Venkatesh
Affiliations:
Department of Computing, Curtin University, Perth, Australia;Department of Computing, Curtin University, Perth, Australia;Department of Computing, Curtin University, Perth, Australia;Department of Computing, Curtin University, Perth, Australia
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Year:
2011

Citing 12
Cited 1

Modern Information Retrieval

Modern Information Retrieval
Model-shared subspace boosting for multi-label classification

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Image retrieval: Ideas, influences, and trends of the new age

ACM Computing Surveys (CSUR)
Flickr tag recommendation based on collective knowledge

Proceedings of the 17th international conference on World Wide Web
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Proceedings of the 25th international conference on Machine learning
Extracting shared subspace for multi-label classification

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking with local regression and global alignment for cross media retrieval

MM '09 Proceedings of the 17th ACM international conference on Multimedia
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Learning social tag relevance by neighbor voting

IEEE Transactions on Multimedia
Bregman Divergence-Based Regularization for Transfer Subspace Learning

IEEE Transactions on Knowledge and Data Engineering
Nonnegative shared subspace learning and its application to social media retrieval

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Harmonizing Hierarchical Manifolds for Multimedia Document Semantics Understanding and Cross-Media Retrieval

IEEE Transactions on Multimedia

Regularized nonnegative shared subspace learning

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.