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Multiple Manifolds Clustering via Local Linear Analysis
2016
Cybernetics and Information Technologies
Clustering on multiple manifolds serves as an analysis of the data lying on multiple manifolds. The smoothness and local linearity of data samples are utilized to define the local linear degree which is motivated by Principal Component Analysis (PCA) and Depth First Search (DFS). Then, Multiple Manifolds Clustering (LMMC) is proposed on the base of the Local Linear Analysis (LLA) via this definition and neighbor-growing algorithm, which are especially effective under the condition of
doi:10.1515/cait-2016-0088
fatcat:huchmpjbtbfvnimx3vmvrvfiny