A New One-class Classifier: Relevant Component Analysis Data Description

It is known that Principal Component Analysis Data Description (PCADD) as a one-class classifier estimates the mean and covariance matrix of the target class objects and uses the Mahalanobis distance so as to fit the target class. However, PCADD just considers the global information of the target class and ignores the local information. In this paper, we introduce the local information into the one-class classifier design and develop a new one-class classifier called Relevant Component Analysis Data Description (RCADD). Concretely, we first divide the target class into some chunklets and each chunklet is made up of the similar objects. Then, the presented RCADD applies the RCA transformation onto the original target space. Finally, the RCADD computes the difference between the original object and the transformation of that object onto the subspace so as to check if the object belongs to the target class. The experimental results demonstrate that the proposed RCADD has a superior performance to the PCADD in terms of classification.