Name
Abstract | ||
|---|---|---|
•This paper proposes an improved deep convolutional embedded clustering algorithm using reliable samples.•This paper designs the new deep clustering model structure and corresponding loss function.•In this study, we select reliable samples with pseudo-labels and pass them to the convolutional neural network for training to get a better clustering model.•We conducted experimental tests on four standard data sets and show the better performance compared to the state-of-the-art clustering algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108611 | Pattern Recognition |
Keywords | DocType | Volume |
Unsupervised clustering,Deep embedded clustering,Autoencoder,Reliable samples | Journal | 127 |
ISSN | Citations | PageRank |
0031-3203 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hu Lu | 1 | 0 | 0.68 |
| Chao Chen | 2 | 5 | 1.77 |
| Hui Wei | 3 | 0 | 0.34 |
| Zhongchen Ma | 4 | 1 | 2.38 |
| Ke Jiang | 5 | 0 | 0.34 |
| Yingquan Wang | 6 | 0 | 0.34 |