Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
Kernel methods and Nonnegative matrix factorization (NMF) are both widely used in data mining and machine learning. The previous one is best known for its capability of transforming data into high dimension feature space, while the latter one is well known for its natural interpretations and good performance. In this paper, we propose a robust kernel NMF approach using L2, 1 norm loss function. Compared with the standard NMF algorithm, the new robust kernel NMF updating algorithm is as elegant and as simple, but with the newly added robustness to handle significantly corrupted datasets because of using L2, 1 norm. Experiments on normal and occluded datasets indicate that robust kernel NMF always perform better than k-means and standard NMF.
Kernel, Robustness, Standards, Linear programming, Convergence, Clustering algorithms, Noise, kernel NMF, L21 norm, nonnegatative matrix factorization, convergence
Zhichen Xia, Chris Ding, Edmond Chow, "Robust Kernel Nonnegative Matrix Factorization", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 522-529, doi:10.1109/ICDMW.2012.141