Outlier detection based on sparse coding and neighbor entropy in high-dimensional space

Outlier detection is an important branch in data mining and plays a vital role in broad range of applications including network-traffic anomaly detection, credit fraud prevention, etc. Based on the assumption that dataset can be approximately reconstructed by linear combinations of dictionary atoms, some detection algorithms initially project the data to a higher dimensional manifold such that data representation becomes sparse. Unlike previous sparse coding based approaches, our method SNOD (Sparse coding and Neighbor entropy based Outlier Detection) can detect local and global outliers and construct neighborhood in a self-manner. Finally, the outlier score of each sample using local reconstruction coefficients is computed. Experiments on several benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.