Name
Title | ||
|---|---|---|
Online Low-Rank Representation Learning for Joint Multi-Subspace Recovery and Clustering. |
Abstract | ||
|---|---|---|
Benefiting from global rank constraints, the low-rank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank m... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2017.2760510 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Heuristic algorithms,Clustering algorithms,Learning systems,Algorithm design and analysis,Robustness,Time complexity,Optimization | Data mining,Online algorithm,Algorithm design,Subspace topology,Computer science,Algorithm,Dynamic data,Cluster analysis,Time complexity,Dynamic problem,Feature learning | Journal |
Volume | Issue | ISSN |
27 | 1 | 1057-7149 |
Citations | PageRank | References |
6 | 0.41 | 29 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Li | 1 | 41 | 2.95 |
| Risheng Liu | 2 | 833 | 59.64 |
| Junjie Cao | 3 | 212 | 18.07 |
| Jie Zhang | 4 | 112 | 7.99 |
| Yu-Kun Lai | 5 | 1025 | 80.48 |
| Xiuping Liu | 6 | 156 | 18.74 |