Recent Advances of Large-Scale Linear Classification This paper is a survey on development of optimization methods to construct linear classifiers suitable for large-scale applications; for some data, accuracy is close to that of nonlinear classifiers.

Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classi- fiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much faster. Recently, many research works have developed efficient optimization methods to construct linear classifiers and ap- plied them to some large-scale applications. In this paper, we give a comprehensive survey on the recent development of this active research area.