Graph Based Multi-Layer K-Means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces
| dc.contributor.author | Tao, Feng | |
| dc.contributor.author | Suresh, Rengan | |
| dc.contributor.author | Votion, Johnathan | |
| dc.contributor.author | Cao, Yongcan | |
| dc.date.accessioned | 2021-04-19T15:26:37Z | |
| dc.date.available | 2021-04-19T15:26:37Z | |
| dc.date.issued | 2021-03-16 | |
| dc.date.updated | 2021-04-19T15:26:38Z | |
| dc.description.abstract | In this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve the 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 develop the multi-layer k-means++ (MLKM) method for data-target association at a 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 p-dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association, extracting cross-local data-target association mathematically, and then analyzing 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, which yields an average data-target association accuracy of 92.2%. | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.identifier | doi: 10.3390/s21062069 | |
| dc.identifier.citation | Sensors 21 (6): 2069 (2021) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/550 | |
| dc.rights | Attribution 4.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | graph theory | |
| dc.subject | sensor networks | |
| dc.subject | data-object association | |
| dc.subject | machine learning | |
| dc.title | Graph Based Multi-Layer K-Means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces | |
| dc.type | Article |