Passive-Aggressive online learning with nonlinear embeddings. (July 2018) - Jorge, Javier; Paredes, Roberto
Highlights: A combination of Passive-Aggressive algorithm and the Max-Out function is proposed. This approach is an alternative to Kernel-based methods in online learning problems. The algorithm does not rely on budget strategies over the set of support vectors. The model is fast and uses nonlinear learned embeddings, avoiding support vectors. This approach has obtained better results in commonly used online learn- ing benchmarks. Abstract: Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time. In these scenarios, online learning is able to learn a model from data that comes continuously. The adaptability, efficiency and scalability of online learning techniques have been gaining interest last years with the increasing amount of data generated every day. In this paper, we propose a novel binary classification approach based on nonlinear mapping functions under an online learning framework. The non-convex optimization problem that arises is split into three different convex problems that are solved by means of Passive-Aggressive Online Learning. We evaluate both the adaptability and generalization of our model through several experiments comparing with the state of the art techniques. We improve significantly the results in several datasets widely used previously by the online learning community.