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A class boundary prediction method by self-representation based pattern selection and geometric-constrained diffusion generation
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DOI：10.1016/j.knosys.2025.114096.png)
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Abstract
En 中文
In data-driven decision systems, boundary patterns are instrumental for delineating distinct categories. However, the scarcity of representative samples that encapsulate the class data distribution often results in a dearth of critical boundary information, impeding the comprehension of data structures and the extraction of pivotal details. To surmount the challenge of predicting boundary patterns amidst insufficient data, this paper proposes a class boundary prediction method by self-representation based pattern selection and geometric-constrained diffusion generation (BP-PSG). It leverages the negative components of samples based on self-representation to distinguish between different edge patterns. A novel conditioning mechanism is proposed for diffusion model that constrains data generation along geometrically meaningful directions. The aggregated data is then subjected to a cycle of evaluation and generation to obtain more potential representative samples that reflect the class data distribution. The ultimate edge patterns derived from this comprehensive dataset are deemed as class boundary. The effectiveness of our method has been validated on both high-dimensional and low-dimensional data, including synthetic data, benchmark datasets, and the RSRAC dataset. Notably, BP-PSG enhances anomaly detection accuracy by 5.44 % to 27.38 % on the benchmark datasets. On the RSRAC dataset, it improves individual emitter identification accuracy by an average of 3.95 % and unknown-class detection accuracy by an average of 11.35 %.
Keywords:
boundary pattern
self-representation
diffusion generation
class boundary
anomaly detection
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
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