Li, WenyeWenyeLiLee, Kin-HongKin-HongLeeProf. LEUNG Kwong Sak2023-03-242023-03-242006Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 4233 LNCS - II, pp. 796 - 8053540464816978-354046481-503029743http://hdl.handle.net/20.500.11861/7588Considering data processing problems from a geometric point of view, previous work has shown that the intrinsic dimension of the data could have some semantics. In this paper, we start from the consideration of this inherent topology property and propose the usage of such a semantic criterion for clustering. The corresponding learning algorithms are provided. Theoretical justification and analysis of the algorithms are shown. Promising results are reported by the experiments that generally fail with conventional clustering algorithms. © Springer-Verlag Berlin Heidelberg 2006.enIntrinsic DimensionFace ImageDimensionality AnalysisExpectation Maximization AlgorithmNeural Information Processing SystemConference Paper10.1007/11893257_88