Local Subspace Based Outlier Detection

Existing studies in Outlier detection mostly focus oil detecting Outliers in full feature space. But most algorithms tend to break down in high-dimensional feature spaces because classes of objects often exist in specific subspace of the original feature space. Therefore, subspace Outlier detection has been recently defined. As a novel Solution to tackle this problem, we propose here a local subspace based outlier detection technique, which uses different subspaces for different objects. Using this concept we adopt local density based outlier detection to cope with high-dimensional data. A broad experimental evaluation shows that this approach yields results of significantly better quality than existing algorithms.