Name
Abstract | ||
|---|---|---|
The L1 -norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function to the ori... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TNNLS.2015.2496964 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Matrix decomposition,Cost function,Recurrent neural networks,Approximation methods,Linear programming | Journal | 27 |
Issue | ISSN | Citations |
2 | 2162-237X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiguang Liu | 1 | 338 | 37.15 |
| Songfan Yang | 2 | 343 | 17.48 |
| Pengfei Wu | 3 | 25 | 6.14 |
| Chunguang Li | 4 | 748 | 63.37 |
| Menglong Yang | 5 | 109 | 10.49 |