Robust principal component analysis based on advanced augmented lagrange multiplier method

Abstract:

To solve the problem that the calculation accuracy of the robust principal component analysis is reduced when the high dimensional data is disturbed by the sparse large noise and Gaussian noise at the same time, this paper proposes the advanced augmented Lagrange multiplier method for the robust principal component analysis. On one hand, we enhance the calculation accuracy by the advanced method which is based on the optimal initialization of the Lagrange multiplier. On the other hand we propose a dual noise convex optimization model for the robust principal component analysis. As the experimental results shown, the proposed advanced method provides an optimal multiplier for the augmented Lagrange multiplier method and enhances the calculation accuracy of the method. Besides, the proposed dual noise model can separate the Gaussian noise and sparse noise from the data clearly and reinforces the robustness of the robust principal component analysis facing with dual noise.