Fast Support Vector Data Description Using K-Means Clustering

Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDDmethod using K-means clustering method. Our method uses divide-and-conquer strategy; trains each decomposed subproblems to get support vectors and retrains with the support vectors to find a global data description of a whole target class. The proposed method has a similar result to the original SVDD and reduces computational cost. Through experiments, we show efficiency of our method.