A Novelty Detection Algorithm in the Presence of Noise

Abstract

: To address the poor performance of novelty detection in the presence of noisy samples, a method named kernel null space discriminant locality preserving projections (KNDLPP) is proposed. Firstly, the training samples are transformed into a high dimensional space through a kernel function implicitly, and different weights are assigned to these samples according to the distance weighted scheme in the UCI datasets, the whole mean AUC of KNDLPP is 90.656%. During the experiments about complex structure on Banana, Moon and 3 UCI datasets, the whole mean AUC of KNDLPP is 91.949%. During the experiments on 2 clean high dimensional datasets for novelty detection, the whole mean AUC of KNDLPP is 86.214%, which is 4 percent higher than the second best algorithm. On 4 UCI datasets with 4 different kinds of noise, the performance of KNDLPP ranks first.