A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency
Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generativ...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-10-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/20/11/830 |
| _version_ | 1798006492035547136 |
|---|---|
| author | Xulun Ye Jieyu Zhao Yu Chen |
| author_facet | Xulun Ye Jieyu Zhao Yu Chen |
| author_sort | Xulun Ye |
| collection | DOAJ |
| description | Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generative clustering approach within the Bayesian nonparametric framework has been proposed. Specifically, our manifold method automatically selects the cluster number with a Dirichlet Process (DP) prior. Then, a DP-based mixture model with constrained Mixture of Gaussians (MoG) is constructed to handle the manifold data. Finally, we integrate our model with the <i>k</i>-nearest neighbor graph to capture the manifold geometric information. An efficient optimization algorithm has also been derived to do the model inference and optimization. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new DP-based manifold method. |
| first_indexed | 2024-04-11T12:55:40Z |
| format | Article |
| id | doaj.art-008bfd549d744e3cb4ec26c7a42d4368 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T12:55:40Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-008bfd549d744e3cb4ec26c7a42d43682022-12-22T04:23:04ZengMDPI AGEntropy1099-43002018-10-01201183010.3390/e20110830e20110830A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph ConsistencyXulun Ye0Jieyu Zhao1Yu Chen2Institute of Computer Science and Technology, Ningbo University, Ningbo 315211, ChinaInstitute of Computer Science and Technology, Ningbo University, Ningbo 315211, ChinaInstitute of Computer Science and Technology, Ningbo University, Ningbo 315211, ChinaMulti-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generative clustering approach within the Bayesian nonparametric framework has been proposed. Specifically, our manifold method automatically selects the cluster number with a Dirichlet Process (DP) prior. Then, a DP-based mixture model with constrained Mixture of Gaussians (MoG) is constructed to handle the manifold data. Finally, we integrate our model with the <i>k</i>-nearest neighbor graph to capture the manifold geometric information. An efficient optimization algorithm has also been derived to do the model inference and optimization. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new DP-based manifold method.https://www.mdpi.com/1099-4300/20/11/830multi-manifold clusteringDirichlet process mixture modelmixture of Gaussiansgraph theory |
| spellingShingle | Xulun Ye Jieyu Zhao Yu Chen A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency Entropy multi-manifold clustering Dirichlet process mixture model mixture of Gaussians graph theory |
| title | A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency |
| title_full | A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency |
| title_fullStr | A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency |
| title_full_unstemmed | A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency |
| title_short | A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency |
| title_sort | nonparametric model for multi manifold clustering with mixture of gaussians and graph consistency |
| topic | multi-manifold clustering Dirichlet process mixture model mixture of Gaussians graph theory |
| url | https://www.mdpi.com/1099-4300/20/11/830 |
| work_keys_str_mv | AT xulunye anonparametricmodelformultimanifoldclusteringwithmixtureofgaussiansandgraphconsistency AT jieyuzhao anonparametricmodelformultimanifoldclusteringwithmixtureofgaussiansandgraphconsistency AT yuchen anonparametricmodelformultimanifoldclusteringwithmixtureofgaussiansandgraphconsistency AT xulunye nonparametricmodelformultimanifoldclusteringwithmixtureofgaussiansandgraphconsistency AT jieyuzhao nonparametricmodelformultimanifoldclusteringwithmixtureofgaussiansandgraphconsistency AT yuchen nonparametricmodelformultimanifoldclusteringwithmixtureofgaussiansandgraphconsistency |