<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 interactions. Instead of addressing problems of complex optimization and K-means operation, LMMC is simple and efficient compared with traditional manifold clustering. The algorithm can achieve superior performance on complex subspace and manifolds clustering datasets. Meanwhile, comparative experiments are given to show the effectiveness and efficiency of this algorithm.</p></abstract>

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 interactions. Instead of addressing problems of complex optimization and K-means operation, LMMC is simple and efficient compared with traditional manifold clustering. The algorithm can achieve superior performance on complex subspace and manifolds clustering datasets. Meanwhile, comparative experiments are given to show the effectiveness and efficiency of this algorithm.

Multiple Manifolds Clustering via Local Linear Analysis

School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169,

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Multiple Manifolds Clustering via Local Linear Analysis"}

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...

Multiple Manifolds Clustering via Local Linear Analysis

Pubblicato online: 25 gen 2017

Pagine: 194 - 206

DOI: https://doi.org/10.1515/cait-2016-0088

Parole chiave
Manifolds learning, clustering algorithm, PCA, DFS, neighborhood

© 2016 Wei Zheng et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Multiple Manifolds Clustering via Local Linear Analysis

Pubblicato online: 25 gen 2017

Pagine: 194 - 206

DOI: https://doi.org/10.1515/cait-2016-0088

Parole chiaveManifolds learning, clustering algorithm, PCA, DFS, neighborhood

© 2016 Wei Zheng et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Manifolds learning, clustering algorithm, PCA, DFS, neighborhood