Title :
Constraint-based semi-supervised dimensionality reduction with conflict detection
Author :
Chen, Binhui ; Bai, Qingyuan
Author_Institution :
Sch. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China
Abstract :
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.
Keywords :
learning (artificial intelligence); conflict detection; constraint-based semisupervised dimensionality reduction; semisupervised learning algorithms; unsupervised distance-based methods; Accuracy; Algorithm design and analysis; Clustering algorithms; Data mining; Machine learning; Software; Symmetric matrices; SSDR; adjustment of constraints; clustering analysis; conflict detection; semi-supervised learning;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6495-1
DOI :
10.1109/BMEI.2010.5639901
