论文标题
基于图形的多层K-均值++（G-MLKM）用于受约束空间中的感觉模式分析
Graph Based Multi-layer K-means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces
论文作者
论文摘要
在本文中，我们专注于开发一种新型的无监督机器学习算法，该算法为基于图形的多层K-MEANS ++（G-MLKM）（G-MLKM），当目标在约束空间上移动时，可以通过传感器获得数据目标关联问题。 G-MLKM没有采用基于统计概率的传统数据目标关联方法，而是通过数据群集解决了问题。我们首先将开发多层K-MEANS ++（MLKM）方法，用于在局部空间的数据目标关联，鉴于简化的约束空间情况。然后提出了p偶数图以表示当局部空间相互联系时的一般约束空间。基于双重图和图理论，我们通过首先了解局部数据目标关联，然后提取跨本地数据目标关联来将MLKM推广到G-MLKM。为了排除不服从物理规则的潜在数据目标关联错误，我们还开发了错误校正机制以进一步提高准确性。进行了许多模拟示例，以证明G-MLKM的性能。
In this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve data-target association problem when targets move on a constrained space and minimal information of the targets can be obtained by sensors. Instead of employing the traditional data-target association methods that are based on statistical probabilities, the G-MLKM solves the problem via data clustering. We first will develop the Multi-layer K-means++ (MLKM) method for data-target association at local space given a simplified constrained space situation. Then a p-dual graph is proposed to represent the general constrained space when local spaces are interconnected. Based on the dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association and then extracting cross-local data-target association mathematically analyze the data association at intersections of that space. To exclude potential data-target association errors that disobey physical rules, we also develop error correction mechanisms to further improve the accuracy. Numerous simulation examples are conducted to demonstrate the performance of G-MLKM.
