Name
Abstract | ||
|---|---|---|
Kernelized methods using multiple kernels have shown better performances in graph-based clustering. But those kernelized methods get affected by the noise present in the data set. Also, only a single view has been used in those kernelized graph-based clustering methods. To address those issues, a novel low-rank multi-view multi-kernel graph-based clustering framework (LRMVMKC) has been proposed in this paper. Where the similarity nature of kernel matrices are exploited by low-rank optimal kernel learning and the clustering performances are boosted by using multiple views that provide different partial information about a given data set. The use of the proposed LRMVMKC framework on different benchmark data sets demonstrates the better performances of the proposed framework over other existing methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/NCC48643.2020.9056006 | 2020 National Conference on Communications (NCC) |
Keywords | DocType | ISBN |
Clustering,Multi-view,Multiple Kernel,Low-Rank,LRMVMKC | Conference | 978-1-7281-5121-2 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Supratim Manna | 1 | 1 | 0.68 |
| Jessy Rimaya Khonglah | 2 | 1 | 0.68 |
| Anirban Mukherjee | 3 | 1 | 0.68 |
| Goutam Saha | 4 | 5 | 5.15 |