Fuzzy transfer learning of human activities in heterogeneous feature spaces

Data-driven machine learning methods usually require large amounts of annotated data to be able to develop high performance learning systems. In practical situations, such large amounts of data are not easily obtainable. Transfer Learning evolved as one of the solutions to this challenge. It aims to make use of knowledge acquired in one domain to facilitate prediction in a target domain. Transfer learning can be a daunting task when feature spaces which require transfer differ in their distribution of information. A case of this is in the application of assisted robotics, where a robot is required to learn a task by mere observation of a human perform the task. The differences in the feature spaces poses a challenge in the effective transfer of such tasks. In this paper, we propose a method of effective transfer learning across heterogeneous feature spaces for the purpose of learning in assisted living environments. A fuzzy latent space exploration is used to obtain mappings of feature spaces. This approach is used in simplifying the learning for an assistive robot seeking to execute human actions.