Name
Title | ||
|---|---|---|
Low Rank Subspace Clustering via Discrete Constraint and Hypergraph Regularization for Tumor Molecular Pattern Discovery. |
Abstract | ||
|---|---|---|
Tumor clustering is a powerful approach for cancer class discovery which is crucial to the effective treatment of cancer. Many traditional clustering methods such as NMF-based models, have been widely used to identify tumors. However, they cannot achieve satisfactory results. Recently, subspace clustering approaches have been proposed to improve the performance by dividing the original space into ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCBB.2018.2834371 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
Tumors,Gene expression,Manifolds,Clustering methods,Data models,Cancer,Self-organizing feature maps | Data modeling,Data set,Pattern recognition,Subspace topology,Computer science,Hypergraph,Linear subspace,Synthetic data,Regularization (mathematics),Artificial intelligence,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 5 | 1545-5963 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Liu | 1 | 115 | 57.13 |
| Yuhu Cheng | 2 | 19 | 8.44 |
| Xuesong Wang | 3 | 267 | 42.23 |
| Xiaoluo Cui | 4 | 1 | 0.36 |
| Yi Kong | 5 | 13 | 3.22 |
| Junping Du | 6 | 789 | 91.80 |