Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning Classification with Both Labeled and Unlabeled Data
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding the Yarowsky Algorithm
Computational Linguistics
A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion
The Journal of Machine Learning Research
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Semi-supervised learning by disagreement
Knowledge and Information Systems
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Feature-based dissimilarity space classification
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Semi-supervised linear discriminant analysis using moment constraints
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Constrained log-likelihood-based semi-supervised linear discriminant analysis
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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A semi-supervised version of classical linear discriminant analysis is presented. As opposed to most current approaches to semi-supervised learning, no additional extrinsic assumptions are made to tie information coming from labeled and unlabeled data together. Our approach exploits the fact that the parameters that are to be estimated fulfill particular relations, intrinsic to the classifier, that link label-dependent with label-independent quantities. In this way, the latter type of parameters, which can be estimated based on unlabeled data, impose constraints on the former and lead to a reduction in variability of the label dependent estimates. As a result, the performance of our semi-supervised linear discriminant is typically expected to improve over that of its regular supervised match. Possibly more important, our semi-supervised linear discriminant analysis does not show the severe deteriorations other approaches frequently display with increasing numbers of unlabeled data. This work recapitulates, corrects, extends, and revises our previous work that has been published as part of the First IAPR TC3 Workshop on Partially Supervised Learning. The main novelty it provides over our earlier work is an affine invariant approach to semi-supervised learning befitting linear discriminant analysis. Besides, more elaborate and convincing experimental evidence of the potential of our general approach is provided. We essentially believe that the general principle of intrinsic constraints is of interest as such and may inspire other novel semi-supervised methods.