Auto-weighted 2-dimensional maximum margin criterion

As a hot topic in machine learning, supervised learning is applied to both classification and recognition frequently. However, parameter-tuning in most supervised methods is a laborious work due to its complexity and unpredictability. In this paper, we propose an auto-weighted approach, termed as auto-weighted 2-dimensional maximum margin criterion, which updates the introduced weight in each iteration automatically to leverage the associated terms, so that the weight becomes insensitive to initialization. In addition, the proposed method extracts features from 2-order data directly, i.e., image data. Moreover, we have an observation that the objective value in the proposed method could directly reflect the performance in classification task under the varying dimensionality, which is much beneficial to selection of the optimal dimensionality. Extensive experiments on several datasets are conducted to validate that our method is of great superiority compared to other approaches.