Embedded Unsupervised Feature Selection

Summary

A novel unsupervised feature selection algorithm EUFS is proposed, which directlyembeds feature selection into a clustering algorithm viasparse learning without the transformation, and the AlternatingDirection Method of Multipliers is used to addresst the optimization problem of EUFS.

Keywords

Feature selection, Artificial intelligence, Computer science, Cluster analysis, Pattern recognition (psychology)

References

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A Survey on Feature SelectionJianyu Miao, Lingfeng Niu — 2016
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Approximate Asymmetric Search for Binary Embedding CodesChih‐Yi Chiu, Yu-Cyuan Liou, Amorntip Prayoonwong — 2016
Towards Robust Semantic Attribute Learning in Visual ComputingLin Chen — 2016
An unsupervised-based dynamic feature selection for classification tasksRomulo de O. Nunes, Carine A. Dantas, Anne M. P. Canuto — 2016
From Tweets to Wellness: Wellness Event Detection from Twitter StreamsMohammad Akbari, Xia Hu, Liqiang Nie — 2016
Coupled Dictionary Learning for Unsupervised Feature SelectionPengfei Zhu, Qinghua Hu, Changqing Zhang — 2016
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Robust Manifold Matrix Factorization for Joint Clustering and Feature ExtractionLefei Zhang, Qian Zhang, Bo Du — 2017
Robust unsupervised feature selection by nonnegative sparse subspace learningWei Zheng, Hui Yan, Jian Yang — 2016
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