EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Mixtures of probabilistic principal component analyzers
Neural Computation
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Viewpoint-Invariant Learning and Detection of Human Heads
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A minimum description length framework for unsupervised learning
A minimum description length framework for unsupervised learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Concept-Based, Personalized Web Information Gathering: A Survey
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Learning Articulated Structure and Motion
International Journal of Computer Vision
Learning parts-based representation for face transition
Proceedings of the international conference on Multimedia
Sparse nonnegative matrix factorization for protein sequence motif discovery
Expert Systems with Applications: An International Journal
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Graph dual regularization non-negative matrix factorization for co-clustering
Pattern Recognition
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
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Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can we determine what are the parts? We attack this problem using learning, proposing a form of generative latent factor model, in which each data dimension is allowed to select a different factor or part as its explanation. This approach permits a range of variations that posit different models for the appearance of a part. Here we provide the details for two such models: a discrete and a continuous one. Further, we show that this latent factor model can be extended hierarchically to account for correlations between the appearances of different parts. This permits modelling of data consisting of multiple categories, and learning these categories simultaneously with the parts when they are unobserved. Experiments demonstrate the ability to learn parts-based representations, and categories, of facial images and user-preference data.