Robust laplacian matrix learning for smooth graph signals
We propose a new method for robust learning Laplacian matrices from observed smooth graph signals in the presence of both Gaussian noise and random valued impulse noise (i.e. outliers) Using the recently developed factor analysis model for representing smooth graph signals, we formulate our learning process as a constrained optimization problem, and adopt the PSi norm for measuring the data fidelity in order to improve robustness. Computational results on three types of synthetic graphs demonstrate that the proposed method outperforms the state of the art methods in terms of commonly used information retrieval metrics, such as F measure, precision, recall and normalized mutual information. In particular, we observed that F measure is improved by up to 16%
2016 IEEE International Conference on Image Processing (ICIP)