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Joint Dictionary Learning for Unsupervised Feature Selection

机译：联合字典学习的无监督特征选择

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摘要

Unsupervised feature selection (UFS) as an effective method to reduce time complexity and storage burden has been widely applied to various machine learning tasks. The selected features should model data distribution, preserve data reconstruction and maintain manifold structure. However, most UFS methods don't consider these three factors simultaneously. Motivated by this, we propose a novel joint dictionary learning method, which handles these three key factors simultaneously. In joint dictionary learning, an intrinsic space shared by feature space and pseudo label space is introduced, which can model cluster structure and reveal data reconstruction. To ensure the sparseness of intrinsic space, the ℓ_1-norm regularization is imposed on the representation coefficients matrix. The joint learning of robust sparse regression model and spectral clustering can select features that maintain data distribution and manifold structure. An efficient algorithm is designed to solve the proposed optimization problem. Experimental results on various types of benchmark datasets validate the effectiveness of our method.
机译：作为减少时间复杂度和存储负担的有效方法，无监督特征选择（UFS）已广泛应用于各种机器学习任务。选定的特征应该对数据分布进行建模，保留数据重建并保持多方面的结构。但是，大多数UFS方法不会同时考虑这三个因素。为此，我们提出了一种新颖的联合字典学习方法，该方法可以同时处理这三个关键因素。在联合词典学习中，引入了特征空间和伪标签空间共享的内在空间，可以对聚类结构进行建模并揭示数据重构。为了确保本征空间的稀疏性，将ℓ_1范数正则化强加在表示系数矩阵上。鲁棒的稀疏回归模型和光谱聚类的联合学习可以选择保持数据分布和流形结构的特征。设计了一种有效的算法来解决所提出的优化问题。在各种类型的基准数据集上的实验结果验证了我们方法的有效性。

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