关键词: 无监督特征选择, 范数约束, 最大熵, 增广拉格朗日

Abstract:

Unsupervised feature selection can reduce the dimension of data and improve the learning performance of algorithms. It is an important research topic in the fields of machine learning and pattern recognition. Different from most methods to solve relaxation problems by introducing sparse regularization into the objective function, an unsupervised feature selection algorithm based on maximum entropy and[l2,0] norm constraints is proposed. Firstly, [l2,0] norm equality constraint with unique definite meaning is used, i.e. the number of features is selected, which does not involve the selection of regularization parameters and avoids parameter adjustment. Secondly, combined with spectral analysis, the local geometric structure of the data is explored and the similarity matrix is adaptively constructed based on the maximum entropy principle. Finally, an alternative iterative optimization algorithm is designed to solve the model by augmented Lagrange function method. Compared with other unsupervised feature selection algorithms on four real data sets, the effectiveness of the proposed algorithm is verified.

Key words: unsupervised feature selection, norm constraint, maximum entropy, augmented Lagrange