A unifying review of linear Gaussian models
Neural Computation
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature extraction via generalized uncorrelated linear discriminant analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generalized component analysis for text with heterogeneous attributes
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Regional Pattern Discovery in Geo-referenced Datasets Using PCA
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Outliers in biometrical data: What's old, What's new
International Journal of Biometrics
A new discriminant principal component analysis method with partial supervision
Neural Processing Letters
Predicting labels for dyadic data
Data Mining and Knowledge Discovery
Improving local descriptors by embedding global and local spatial information
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Classification probabilistic PCA with application in domain adaptation
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Pattern Recognition Letters
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is however not able to use this information. The problem is more interesting if only part of the input data are labeled, i.e., in a semi-supervised setting. In this paper we propose a supervised PCA model called SPPCA and a semi-supervised PCA model called S2PPCA, both of which are extensions of a probabilistic PCA model. The proposed models are able to incorporate the label information into the projection phase, and can naturally handle multiple outputs (i.e., in multi-task learning problems). We derive an efficient EM learning algorithm for both models, and also provide theoretical justifications of the model behaviors. SPPCA and S2PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.