英文摘要： Multi-instance multi-label learning is a machine learning framework proposed recently for solving the problem of multi-semantic data. Because it can provide a possibility for explaining why a concerned sample has the certain class labels, multi-instance multi-label learning framework is attracting more and more attention. Gaussian process model is a kernel method that has many merits such as being implemented easily, adaptively discovering the relationship among variables. This project aims at developing a novel multi-instance multi-label learning algorithm based on Gaussian process model for solving the problem of large-scale incompletely annotated multi-semantic data. It includes research to solve the problem of simultaneously describing the relationship between instances and labels as well as the relationship among labels by designing a new Gaussian process model, to solve the large-scale training data problem by proposing an solving approach with lower computational cost for Gaussian process model based on stochastic variational inference, to solve the incompletely annotated data problem by developing a two-step strategy based on ideas of positive and unlabeled learning. Based on Gaussian process model, we not only develop a model that can simultaneously describe the relationship between instances and labels as well as the relationship among labels, which is a key problem for developing multi-instance multi-label learning algorithm, but also solve the problem that kernel methods is difficult to process large-scale training data. This project will promote the application of multi-instance multi-label learning in big data.
英文关键词： Weak label learning;Multi-instance learning;Multi-label learning