Tensor and Hypergraph Methods in Fitting Visual Data

拟合视觉数据中的张量和超图方法

• 批准号: DP200103448
• 负责人: Prof David Suter
• 金额: $ 26.69万
• 依托单位: Edith Cowan University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-01-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200103448
• 关键词: Tensor; Hypergraph; Methods; Fitting; Visual

This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.

这一提议将把一类重要的聚类（提取符合几何模型的数据）放在一个更坚实的理论基础上。这将有助于更好地理解如何认证结果、效率、可靠性等。所考虑的聚类类型与机器学习和计算机视觉中的许多问题以及数据挖掘和各种其他设置相关。