Robust Manifold Non-Negative Matrix Factorization
2016-08-23
0 0 0
no vote
Other
Earn points
Nonnegative matrix factorization (NMF) has become one of the most widely used clustering techniques for exploratory data analysis. However, because each data point into the square of the residual error objective function and some outsider error is easy to dominate the objective function. In this paper, we propose a nonnegative matrix factorization of robust manifolds using ℓ 2,1-norm and integrating NMF and spectral clustering (rmnmf) under the same clustering framework. We also point out that for the existing nonnegative matrix factorization methods, the uniqueness problem is solved, and propose additional orthogonal constraints to solve this problem. The new constraint is no longer valid compared with the conventional auxiliary function method. We solve this difficult optimization problem through the augmented Lagrangian method (ALM) algorithm, and transform the novel original constrained optimization problem to a multivariate constrained problem. The new objective function can then be decomposed into several subproblems, each of which has a closed form solution. More importantly, we reveal the robust K-means and spectral join clustering of our method, and prove its theoretical significance. All empirical results show the effectiveness and extensive experiments of our method on nine benchmark datasets.
matlab
矩阵
分解
Related Source Codes
GMSK Linear Receiver
0
0
no vote
NSGA-II algorithm
0
0
no vote
NSGA-III multi-objective optimization algorithm
0
0
no vote
Compressed sensing example
0
0
no vote
CFAR detector example
0
0
no vote
No comment