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Unsupervised Feature Selection With Flexible Optimal Graph
IEEE Transactions on Neural Networks and Learning SystemsIn the unsupervised feature selection method based on spectral analysis, constructing a similarity matrix is a very important part. In existing methods, the linear low-dimensional projection used in the process of constructing the similarity matrix is too hard, it is very challenging to construct a reliable similarity matrix.
Hong Chen +3 more
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Joint learning of graph and latent representation for unsupervised feature selection
Applied intelligence (Boston), 2023Xijiong Xie, Zhiwen Cao, Feixiang Sun
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Understanding-Oriented Unsupervised Feature Selection
2017In many image processing and pattern recognition problems, visual contents of images are currently described by high-dimensional features, which are often redundant and noisy. Toward this end, we propose two novel understanding-oriented unsupervised feature selection schemes.
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Consensus cluster structure guided multi-view unsupervised feature selection
Knowledge-Based Systems, 2023Zhiwen Cao +3 more
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Unsupervised Learning Methods for Molecular Simulation Data
Chemical Reviews, 2021Aldo Glielmo +2 more
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Structure learning with consensus label information for multi-view unsupervised feature selection
Expert systems with applications, 2023Zhiwen Cao, Xijiong Xie
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Unsupervised Feature Selection with Feature Clustering
2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2012Yiu-Ming Cheung, Hong Jia
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A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning
ACM Computing Surveys, 2022Angelo Genovese +2 more
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