Broad Graph-Based Non-Negative Robust Continuous Clustering
Feng,Qiying1; Chen,C. L.Philip2,3,4; Chen,Long1
Source PublicationIEEE Access
AbstractRecently, the robust continuous clustering (RCC) has been proposed for unsupervised data classification. The RCC algorithm integrates representation learning and clustering by seeking the balance of the distance of data between intra-cluster and inter-cluster. But the inter-cluster distance in RCC highly depend on the pairwise graph of neighbors, which are constructed by the less-expressive original data. This hampers the performance of RCC on complex epically high-dimensional data. Encouraged by the hybrid feature learning and universal approximation capabilities of the broad learning system (BLS), we first propose a broad graph-based robust continuous clustering algorithm to upgrade RCC. The proposed algorithm measures the distance of the pairwise data with the feature learned from the BLS when constructing the graph. Then to further enhance the clustering performance of the RCC on high-dimensional data, we embed the non-negative matrix factorization (NMF) into the broad graph-based RCC algorithm. By resolving the original data into the basis matrix and coefficient matrix and performing the broad graph-based RCC on the coefficient matrix, the proposed approach takes full advantages of the abundant representation from the BLS and the sparsity and interpretability of NMF coefficients. We verified the proposed algorithms on the synthetic dataset, the UCI dataset and some real-world high dimension datasets. All the empirical results show the proposed algorithms outperform the baselines and improve the clustering performance of RCC effectively.
Keywordbroad learning system non-negative matrix factorization representation learning Robust continuous clustering
URLView the original
Scopus ID2-s2.0-85088860565
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorChen,Long
Affiliation1.Department of Computer and Information Science,Faculty of Science and Technology,University of Macau,999078,Macao
2.School of Computer Science and Engineering,South China University of Technology,Guangzhou,510641,China
3.College of Navigation,Dalian Maritime University,Dalian,116026,China
4.Faculty of Science and Technology,University of Macau,99999,Macao
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Feng,Qiying,Chen,C. L.Philip,Chen,Long. Broad Graph-Based Non-Negative Robust Continuous Clustering[J]. IEEE Access,2020,8:121693-121704.
APA Feng,Qiying,Chen,C. L.Philip,&Chen,Long.(2020).Broad Graph-Based Non-Negative Robust Continuous Clustering.IEEE Access,8,121693-121704.
MLA Feng,Qiying,et al."Broad Graph-Based Non-Negative Robust Continuous Clustering".IEEE Access 8(2020):121693-121704.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Feng,Qiying]'s Articles
[Chen,C. L.Philip]'s Articles
[Chen,Long]'s Articles
Baidu academic
Similar articles in Baidu academic
[Feng,Qiying]'s Articles
[Chen,C. L.Philip]'s Articles
[Chen,Long]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Feng,Qiying]'s Articles
[Chen,C. L.Philip]'s Articles
[Chen,Long]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.