Further, assume that the components of the noised feature vectorg(x) are independent.
the key idea behind feature noising is to artificially corrupt the feature vector f (y, x) randomly into somef (y, x) and then maximize the average log-likelihood of y given these corrupted featuresthe motivation is to choose predictors θ that are robust to noise (missing words for example).