Nonlinear Transfer Distance Metric Learning for Gleaning Knowledge from the Crowd

该项目将开发非线性迁移距离度量学习算法，用于训练和测试不独立和相同分布的样本，或来自不同的实例空间。众包的新理论基础将为NBN、社会和安全服务等目的带来创新的智能系统，并与硬件技术的发展保持同步。其成果包括在社交网络、互联网和气候变化方面的应用，以及帮助打击犯罪和恐怖主义的视频监控。这项创新研究将显著造福澳大利亚的经济、环境和社会，并将保持澳大利亚在机器学习和计算机视觉领域的全球领先地位。

This project will develop nonlinear transfer distance metric learning algorithms for training and test samples that are not independent and identically distributed, or from different instance spaces. New theoretical foundations for crowd-sourcing will lead to innovative intelligent systems for such purposes as the NBN, social, and security services, and keep pace with developments in hardware technology. The outcomes include applications in social networks, the Internet, and climate change, as well as video surveillance to help combat crime and terrorism. The innovative research will significantly benefit Australia’s economy, environment and society, and will maintain Australia's global leading role in the machine learning and computer vision.