用统一算法生成合成数据并自动标注，提升AI训练效果。
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
- 基于广义线坐标可视化高维数据，实现无损多维度表示
- 结合静态与动态圆坐标，有效检测异常值并揭示属性分布
- 在真实数据上验证，显著提升分类器性能，适合数据稀缺场景
人工智能与机器学习模型的开发与部署面临训练数据不足的关键挑战。本文提出一种统一的合成数据生成（SDG）与自动化数据标注（ADL）方法，采用统一的SDG-ADL算法。该方法利用广义线坐标（GLC）对多维（n-D）数据进行无损可视化，依赖可逆的GLC特性，在多个GLC中呈现n-D数据。本文首次引入静态与动态形式的圆坐标，配合平行坐标与移位成对坐标使用，每种GLC均体现独特的数据特性，如多属性分布和异常值检测。该方法在计算机软件中实现为动态坐标可视化系统（DCVis），并通过真实数据案例研究，评估其对分类器的影响。
原文摘要 · Abstract (English)
Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG) and automated data labeling (ADL) with a unified SDG-ADL algorithm. SDG-ADL uses multidimensional (n-D) representations of data visualized losslessly with General Line Coordinates (GLCs), relying on reversible GLC properties to visualize n-D data in multiple GLCs. This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as interattribute n-D distributions and outlier detection. The approach is interactively implemented in computer software with the Dynamic Coordinates Visualization system (DCVis). Results with real data are demonstrated in case studies, evaluating impact on classifiers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。