Name
Abstract | ||
|---|---|---|
Traditional clustering algorithms have some limitations, which are sensitive to noise and mostly applicable to convex data sets. To solve these problems, the paper proposes a novel algorithm combining spectral clustering with feature selection. Specifically, the loss item is marked with a root that can reduce the deviation value then improve the robustness of the model. And in the algorithm optimization, there is one parameter is represented by other known parameters, which can reduce the time of parameter adjustment. Then, the regular terml2,p-norm is applied to reduce the influence of noise and redundant features and prevent the model from overfitting. Finally, Laplace matrix is constructed by kNN algorithm which is used to learn subspace and to preserve the local structure among samples, and the data after dimension reduction is used to spectral clustering. Experimental analysis on 10 benchmark datasets show that the proposed algorithm is more outperformed than the algorithms of the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11063-020-10297-6 | NEURAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Spectral clustering, Sparse learning, Locally preserved projection, kNN | Journal | 52 |
Issue | ISSN | Citations |
3 | 1370-4621 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qimin Luo | 1 | 0 | 0.34 |
| Guoqiu Wen | 2 | 46 | 4.62 |
| Leyuan Zhang | 3 | 2 | 2.39 |
| Mengmeng Zhan | 4 | 1 | 2.38 |