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.
|Journal||ACM Transactions on Knowledge Discovery from Data|
|State||Published - Mar 2017|
Bibliographical noteFunding Information:
This work is supported by the National Science Foundation, under grant IIS-1218712, National Natural Science Foundation of China, under grant 11241005, and Shanxi Scholarship Council of China, under grant 2015-093.
© 2017 ACM.
- Nonnegative factorization
- Robust principal component analysis
ASJC Scopus subject areas
- Computer Science (all)