Constraint-Based Semi-Supervised Dimensionality Reduction with Conflict Detection

Most existing typical semi-supervised learning algorithms focused on the results of learning while facing the conflict on constraints. And most solutions use unsupervised distance-based methods to adjust the conflicting constraints on the information by recalculating the samples distance. This paper presents a constraint-based semi-supervised dimensionality reduction algorithm with conflict detection, called CDSSDR, which uses the information of priori constraints to adjust the contradictions in the constraints. It avoids the use of unsupervised methods to adjust the prior knowledge.