MIML-AI: Mixed-supervision multi-instance multi-label learning with auxiliary information
Tarn Nguyen, Raviv Raich, Xiaoli Z. Fern, Anh Thi Lan Pham · 2017
Manual labeling of individual instances is time-consuming. This is commonly resolved by labeling a bag-of-instances with a single common label or label-set. However, this approach is still time-costly for large datasets. In this paper, we propose a mixed-supervision multi-instance multi-label learning model for learning from easily available meta data information (MIML-AI). This auxiliary information is normally collected automatically with the data, e.g., an image location information or a document author name. We propose a discriminative graphical model with exact inferences to train a classifier based on auxiliary label information and a small number of labeled bags. This strategy utilizes meta data as means of providing a weaker label as an alternative to intensive manual labeling. Experiment on real data illustrates the effectiveness of our proposed method relative to current approaches, which do not use the information from bags that contain only meta-data label information.