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Most existing feature selection methods select the top-ranked features according to certain criterion. However, without considering the redundancy among the features, the selected ones are frequently highly correlated with each other, which is detrimental to the performance. To tackle this problem, we propose a framework regarding adaptive redundancy minimization (ARM) for the feature selection. Unlike other feature selection methods, the proposed model has the following merits: (1) The redundancy matrix is adaptively constructed instead of presetting it as the priori information. (2) The proposed model could pick out the discriminative and non-redundant features via minimizing the global redundancy of the features. (3) ARM can reduce the redundancy of the features from both supervised and unsupervised perspectives.