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Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
[article]
2019
arXiv
pre-print
Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional
arXiv:1912.05458v1
fatcat:ncvvwekf7fgbljhhpgvn7n5oem