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
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Neural Computation
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
Discriminative Common Vectors for Face Recognition
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
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and 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
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Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis algorithm called SSDA$_{\mathit{CCCP}}$. We utilize unlabeled data to maximize an optimality criterion of LDA and use the constrained concave-convex procedure to solve the optimization problem. The optimization procedure leads to estimation of the class labels for the unlabeled data. We propose a novel confidence measure for selecting those unlabeled data points with high confidence. The selected unlabeled data can then be used to augment the original labeled data set for performing LDA. We also propose a variant of SSDA$_{\mathit{CCCP}}$, called M-SSDA$_{\mathit{CCCP}}$, which adopts the manifold assumption to utilize the unlabeled data. Extensive experiments on many benchmark data sets demonstrate the effectiveness of our proposed methods.