Regularisation via Von Neumann Entropy for Matrix Completion

通过冯诺依曼熵进行正则化以完成矩阵

• 批准号: 1782548
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1782548
• 关键词: Regularisation; via; Von; Neumann; Entropy

Matrix completion is an area of machine learning with very immediate and practical applications in a business setting. The most notable being recommendation systems such as the so-called 'Netflix problem' in which a user's preferences for a given set of objects are inferred under the assumption that the data may be represented by a partially-known low-rank matrix. The aim of this research is to develop new, efficient algorithms with low mistake bounds for matrix completion in the context of online learning with a focus on entropic regularisation techniques. There's a possibility that parts of this research may be done in collaboration with Siemens UK. This research aligns very closely with EPSRC's research area of Artificial Intelligence Technologies.

矩阵补全是机器学习的一个领域，在商业环境中具有非常直接和实际的应用。最值得注意的是推荐系统，例如所谓的“Netflix问题”，其中用户对给定对象集的偏好是在假设数据可以由部分已知的低秩矩阵表示的情况下推断的。本研究的目的是开发新的，高效的算法，低错误界的矩阵完成的背景下，在线学习的重点是熵正则化技术。这项研究的一部分可能是与西门子英国公司合作完成的。这项研究与EPSRC的人工智能技术研究领域非常接近。

项目成果

Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

神经信息处理系统的进展 34 (NeurIPS 2021)

• 发表时间: 2021
• 作者: Robinson J

Online Prediction of Switching Graph Labelings with Cluster Specialists

与集群专家一起在线预测切换图标签

• 发表时间: 2019
• 作者: Herbster M