Additive Noise Model Structure Learning Based on Spatial Coordinates

Abstract A new algorithm named SCB (Spatial Coordinates Based) algorithm is presented for structure learning of additive noise model, which can effectively deal with nonlinear arbitrarily distributed data. This paper makes three specific contributions. Firstly, SC (Spatial Coordinates) coefficient is proposed to use as a standard of independence test and CSC (Conditional Spatial Coordinates) coefficient as a standard of conditional independence test. Secondly, it is proved that the CSC coefficient conforms to the standard normal distribution and the HSIC independence test can be regarded as a special case of the SC coefficient. Finally, based on the SC coefficient, the SCB algorithm is proposed, and the experimental comparison with some existing algorithms on seven classical networks shows that the SCB algorithm has better performance. In particular, SCB algorithm can deal with large sample, high dimensional nonlinear data, and maintain good accuracy and time performance.