In Multiple Instance Learning (MIL), each entity is normally expressed as a set of instances. Most of the current MIL methods only deal with the case when each instance is represented by one type of features. However, in many real world applications, entities are often described from several different information sources/views. For example, when applying MIL to image categorization, the characteristics of each image can be derived from both its RGB features and SIFT features. Previous research work has shown that, in traditional learning methods, leveraging the consistencies between different information sources could improve the classification performance drastically. Out of a similar motivation, to incorporate the consistencies between different information sources into MIL, we propose a novel research framework - Multi-Instance Learning from Multiple Information Sources (MI2LS). Based on this framework, an algorithm - Fast MI2LS (FMI2LS) is designed, which combines Constraint Concave-Convex Programming (CCCP) method and an adapted Stoachastic Gradient Descent (SGD) method. Some theoretical analysis on the optimality of the adapted SGD method and the generalized error bound of the formulation are given based on the proposed method. Experimental results on document classification and a novel application - Insider Threat Detection (ITD), clearly demonstrate the superior performance of the proposed method over state-of-The-Art MIL methods.