Interpretation and Generalization of Score Matching

Understand

Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions.

• In this paper, we study two issues that have not been completely resolved for score matching.
• First, we provide a formal link between maximum likelihood and score matching.
• Our analysis shows that score matching finds model parameters that are more robust with noisy training data.