Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
This work addresses the challenge of improving classification performance in semi-supervised LDA for pattern recognition, but it appears incremental as it builds on existing methods without claiming broad breakthroughs.
The paper tackles the problem of semi-supervised linear discriminant analysis (LDA) by comparing traditional Expectation Maximization approaches with constraint-based methods and proposing a new approach using implicit constraints, finding that constraint-based methods are more robust to model misspecification and may outperform alternatives in terms of log-likelihood of unseen objects.
Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-supervised variants have been proposed. Using any one of these methods is not guaranteed to outperform the supervised classifier which does not take the additional unlabeled data into account. In this work we compare traditional Expectation Maximization type approaches for semi-supervised linear discriminant analysis with approaches based on intrinsic constraints and propose a new principled approach for semi-supervised linear discriminant analysis, using so-called implicit constraints. We explore the relationships between these methods and consider the question if and in what sense we can expect improvement in performance over the supervised procedure. The constraint based approaches are more robust to misspecification of the model, and may outperform alternatives that make more assumptions on the data, in terms of the log-likelihood of unseen objects.