Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection
Researchers have introduced Two-Stage LKPLO, a novel multi-stage framework designed to improve outlier detection in multi-modal data. This approach overcomes limitations of traditional methods by replacing fixed statistical metrics with adaptive loss functions and incorporating both global kernel PCA for linearization and a local clustering stage for multi-modal distributions. Experiments on benchmark datasets demonstrate that Two-Stage LKPLO achieves state-of-the-art performance, significantly outperforming existing methods on complex and multi-cluster data. AI