Unsupervised feature selection (UFS) can enhance the generalization ability, reduce the computational and storage requirements, and accelerate the training speed by removing irrelevant or redundant features. Subspace learning based UFS has garnered widespread attention, however, many fail to fully leverage the intrinsic statistical information of data when designing a distance metric to measure the distance between the original space and its subspace, leading to the suboptimal solutions. Moreover, some methods frequently overlook feature redundancy or rely heavily on prior knowledge, which may deviate from the true case. To address these limitations, this paper proposes a redundancy-aware UFS approach in the framework of sparse subspace learning, which uses variance-covariance distance as loss function and the l2,1 norm to promote sparsity. Meanwhile, subspace learning minimizes the distance between the original and the target space, and maximizes the projection variance of the data simultaneously. To effectively eliminate redundancy, we fuse multiple relationship metrics of features into an accurate and comprehensive one, which is further used to guide low-redundancy learning. Furthermore, we propose to use Alternating Direction Method of Multiplier (ADMM) to optimize the resulting model, which is often effective and efficient. The extensive experimental results on multiple public datasets demonstrate the effectiveness and superiority.
原文链接
分享
引用
