Name
Abstract | ||
|---|---|---|
Matrix factorization is often used for data representation in many data mining and machine-learning problems. In particular, for a dataset without any negative entries, nonnegative matrix factorization (NMF) is often used to find a low-rank approximation by the product of two nonnegative matrices. With reduced dimensions, these matrices can be effectively used for many applications such as clustering. The existing methods of NMF are often afflicted with their sensitivity to outliers and noise in the data. To mitigate this drawback, in this paper, we consider integrating NMF into a robust principal component model, and design a robust formulation that effectively captures noise and outliers in the approximation while incorporating essential nonlinear structures. A set of comprehensive empirical evaluations in clustering applications demonstrates that the proposed method has strong robustness to gross errors and superior performance to current state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3003730 | TKDD |
Keywords | Field | DocType |
Nonnegative factorization,robust principal component analysis,manifold,clustering | Pattern recognition,Matrix (mathematics),Computer science,Matrix decomposition,Outlier,Robustness (computer science),Robust principal component analysis,Non-negative matrix factorization,Artificial intelligence,Cluster analysis,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
11 | 3 | 1556-4681 |
Citations | PageRank | References |
8 | 0.46 | 39 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chong Peng | 1 | 288 | 20.54 |
| Zhao Kang | 2 | 166 | 9.55 |
| Yunhong Hu | 3 | 28 | 3.66 |
| Jie Cheng | 4 | 95 | 5.77 |
| Qiang Cheng | 5 | 286 | 17.77 |