Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection

• Published in: IEEE access Vol. 5; pp. 8792 - 8803
• Main Authors: Zhou, Wei, Wu, Chengdong, Yi, Yugen, Luo, Guoliang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Clustering algorithms; Feature extraction; Feature selection; feature self-representation; Graphical representations; image recognition and clustering; Linear programming; Manifolds; Mathematical model; Regularization; Robustness; structure preserving; Unsupervised feature selection

Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR). In this algorithm, each feature in high-dimensional...