Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Comparison of approximate methods for handling hyperparameters
Neural Computation
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised probabilistic principal component analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Semi-supervised Discriminant Analysis Via CCCP
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Extending kernel fisher discriminant analysis with the weighted pairwise chernoff criterion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Probabilistic linear discriminant analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Linear discriminant analysis (LDA) is a commonly used method for dimensionality reduction. Despite its successes, it has limitations under some situations, including the small sample size problem, the homoscedasticity assumption that different classes have the same Gaussian distribution, and its inability to produce probabilistic output and handle missing data. In this paper, we propose a semi-supervised and heteroscedastic extension of probabilistic LDA, called S2HPLDA, which aims at overcoming all these limitations under a common principled framework. Moreover, we apply automatic relevance determination to determine the required dimensionality of the low-dimensional space for dimensionality reduction. We empirically compare our method with several related probabilistic subspace methods on some face and object databases. Very promising results are obtained from the experiments showing the effectiveness of our proposed method.