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A comparison of optimization methods and software for large-scale L1-regularized linear classification. (English) Zbl 1242.62065
Summary: Large-scale linear classification is widely used in many areas. The L1-regularized form can be applied for feature selection; however, its non-differentiability causes more difficulties in training. Although various optimization methods have been proposed in recent years, these have not yet been compared suitably. We first broadly review existing methods. Then we discuss state-of-the-art software packages in detail and propose two efficient implementations. Extensive comparisons indicate that carefully implemented coordinate descent methods are very suitable for training large document data.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62-04 Software, source code, etc. for problems pertaining to statistics
68T05 Learning and adaptive systems in artificial intelligence
Software:
LASSO; SVMlight
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