Unsupervised Feature Selection with Structured Graph Optimization

By Feiping Nie, Wei Zhu, Xuelong Li. AAAI Conference on Artificial Intelligence, 2016

329 citations recorded by the source corpus.

Since amounts of unlabelled and high-dimensional data needed to be processed, unsupervised feature selection has become an important and challenging problem in machine learning. Conventional embedded unsupervised methods always need to construct the similarity matrix, which makes the selected features highly depend on the learned structure. However real world data always contain lots of noise samples and features that make the similarity matrix obtained by original data can't be fully relied. We propose an unsupervised feature selection approach which performs feature selection and local structure learning simultaneously, the similarity matrix thus can be determined adaptively. Moreover, we constrain the similarity matrix to make it contain more accurate information of data structure, thus the proposed approach can select more valuable features. An efficient and simple algorithm is derived to optimize the problem. Experiments on various benchmark data sets, including handwritten digit data, face image data and biomedical data, validate the effectiveness of the proposed approach.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a3090741a8c94a49712105355e3d1690c20a49b2

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In Computer Vision and Pattern Recognition

In Molecular Biology

Citations in the corpus

A review of unsupervised feature selection methods (2019, methodology): “In Nie et al. (2016) a sparse learning based method called SOGFS (Structured Optimal Graph Feature Selection)which simultaneously performs feature selection and local structure learning, was proposed.”
Feature Selection (2016, background): “Many `2,1-norm regularization based feature selection methods have been proposed over the past decade [Zhao et al. 2010; Gu et al. 2011c; Yang et al. 2011; Hou et al. 2011; Li et al. 2012; Qian and Zhai 2013; Shi et al. 2014; Liu et al. 2014; Du and Shen 2015; Jian et al. 2016; Liu et al. 2016b; Nie”