Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The nature of statistical learning theory
The nature of statistical learning theory
Learning in graphical models
An introduction to variational methods for graphical models
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
The Journal of Machine Learning Research
A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised topic modeling for image annotation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Graph embedding with constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Biologically inspired feature manifold for scene classification
IEEE Transactions on Image Processing
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Laplacian Regularized Gaussian Mixture Model for Data Clustering
IEEE Transactions on Knowledge and Data Engineering
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
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Image annotation is a typical area where there are multiple types of attributes associated with each individual image. In order to achieve better performance, it is important to develop effective modeling by utilizing prior knowledge. In this article, we extend the graph regularization approaches to a more general case where the regularization is imposed on the factorized variational distributions, instead of posterior distributions implicitly involved in EM-like algorithms. In this way, the problem modeling can be more flexible, and we can choose any factor in the problem domain to impose graph regularization wherever there are similarity constraints among the instances. We formulate the problem formally and show its geometrical background in manifold learning. We also design two practically effective algorithms and analyze their properties such as the convergence. Finally, we apply our approach to image annotation and show the performance improvement of our algorithm.