Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection

• URL: http://arxiv.org/abs/2510.24043v3
• Date: Mon, 03 Nov 2025 00:07:17 GMT
• Title: Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection
• Authors: Akira Tamamori,
• Abstract summary: Two-Stage LKPLO is a novel multi-stage outlier detection framework.<n>It overcomes the coexisting limitations of conventional projection-based methods.<n>It achieves state-of-the-art performance on challenging datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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; and (3) a subsequent local clustering stage to handle multi-modal distributions. Comprehensive 5-fold cross-validation experiments on 10 benchmark datasets, with automated hyperparameter optimization, demonstrate that Two-Stage LKPLO achieves state-of-the-art performance. It significantly outperforms strong baselines on datasets with challenging structures where existing methods fail, most notably on multi-cluster data (Optdigits) and complex, high-dimensional data (Arrhythmia). Furthermore, an ablation study empirically confirms that the synergistic combination of both the kernelization and localization stages is indispensable for its superior performance. This work contributes a powerful new tool for a significant class of outlier detection problems and underscores the importance of hybrid, multi-stage architectures.

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