Sufficient Statistics: an Example

Abstract

The feature selection problem is considered resulting from the transformation x = Bz where B is a k by n matrix of rank k and k is or = to n. Such a transformation can be considered to reduce the dimension of each observation vector z, and in general, such a transformation results in a loss of information. In terms of the divergence, this information loss is expressed by the fact that the average divergence D sub B computed using variable x is less than or equal to the average divergence D computed using variable z. If D sub B = D, then B is said to be a sufficient statistic for the average divergence D. If B is a sufficient statistic for the average divergence, then it can be shown that the probability of misclassification computed using variable x (of dimension k is or = to n) is equal to the probability of misclassification computed using variable z. Also included is what is believed to be a new proof of the well known fact that D is or = to D sub B. Using the techniques necessary to prove the above fact, it is shown that the Brattacharyya distance as measured by variable x is less than or equal to the Brattacharyya distance as measured by variable z.