Original paper
Outlier detection based on sparse coding and neighbor entropy in high-dimensional space
Published: May 11, 2020
Abstract
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...
Paper Details
Title
Outlier detection based on sparse coding and neighbor entropy in high-dimensional space
Published Date
May 11, 2020
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