Name
Abstract | ||
|---|---|---|
•An ATV-NMF model including two regularization items, is presented for non-negative matrix factorization.•The adaptive total variation can adaptively choose the anisotropic smoothing scheme based on the gradient information of data.•Graph regularized can discover intrinsic geometrical and structure information.•The convergence analysis of the proposed ATV-NMF algorithm is provided. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.patrec.2017.08.027 | Pattern Recognition Letters |
Keywords | Field | DocType |
Adaptive total variation,Non-negative matrix factorization,Manifold learning | Pattern recognition,Incomplete Cholesky factorization,Matrix decomposition,Smoothing,Artificial intelligence,Non-negative matrix factorization,Factorization,Incomplete LU factorization,Dixon's factorization method,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
98 | C | 0167-8655 |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengcai Leng | 1 | 18 | 6.83 |
| Guorong Cai | 2 | 8 | 3.85 |
| Dongdong Yu | 3 | 63 | 7.07 |
| Zongyue Wang | 4 | 38 | 6.67 |