The Importance of Suppressing Complete Reconstruction in Autoencoders for Unsupervised Outlier Detection

no code implementations • 6 Nov 2022 • Yafei Shen, Ling Yang

The core idea of our scheme is that in order to better detect high leverage points, we should suppress the complete reconstruction of the dataset to convert high leverage points into influential points, and it is also necessary to ensure that the differences between the eigenvalues of the covariance matrix of the original dataset and their corresponding reconstructed results in the direction of each principal component are equal.

Outlier Detection regression