Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection
arXiv:2510.24043v4 Announce Type: replace Abstract: This paper presents Two-Stage LKPLO, a novel multi-stage outlier detection framework that overcomes the coexisting limitations of conventional projection-based methods: their reliance on a fixed statistical metric and their assumption of a single data structure. Our framework uniquely synthesizes three key concepts: (1) a generalized loss-based outlyingness measure (PLO) that replaces the fixed metric with flexible, adaptive loss functions like our proposed SVM-like loss; (2) a global kernel PCA stage to linearize non-linear data structures;
The paper addresses a long-standing challenge in anomaly detection by proposing a two-stage method that overcomes limitations of traditional techniques in complex, multi-modal datasets, reflecting ongoing advancements in AI interpretability and robustness.
This development improves outlier detection reliability, which is critical for applications ranging from cybersecurity and fraud detection to scientific discovery and industrial quality control, preventing costly errors or missed insights.
The proposed Two-Stage LKPLO framework offers more robust and adaptable outlier detection capabilities, moving beyond fixed statistical metrics and single data structure assumptions.
- · AI researchers
- · Cybersecurity sector
- · Financial fraud detection
- · Industrial anomaly detection
- · Legacy outlier detection methods
- · Systems reliant on simple thresholding
Improved accuracy in identifying anomalous data points across diverse applications.
Reduced false positives and negatives in critical AI-driven decision-making systems.
Enhanced trust and adoption of AI systems in sensitive areas where data integrity is paramount.
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Read at arXiv cs.LG