Proximity-based density description with regularized reconstruction algorithm for anomaly detection

This study addresses unsupervised anomaly detection using one-class classification, which constructs a decision boundary to determine if a new instance belongs to the target class. Existing one-class classification methods often fail in real-world scenarios due to their sensitivity to noise and inability to handle complex structures. We propose a proximity-based density description with a regularized reconstruction algorithm to overcome these limitations. Our method defines density-descriptive coefficients to reconstruct initial density and derives optimal coefficients by minimizing reconstruction error subject to sparsity and smoothness constraints. The sparsity constraint reduces noise effects, while the smoothness constraint encourages a flexible decision boundary. We evaluate our algorithm on benchmark datasets and compare it to existing methods, demonstrating superior performance.

Automated summary

This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.