Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of features, which are often redundant and noisy. Feature selection is one main technique for dimensionality reduction that involves identifying a subset of the most useful features. In...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 26; číslo 9; s. 2138 - 2150
Hlavní autori:	Li, Zechao, Liu, Jing, Yang, Yi, Zhou, Xiaofang, Lu, Hanqing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algebra Algorithm design and analysis Algorithms and association rules classification Clustering Clustering algorithms Computing Methodologies Correlation Database Applications Database Management Design Methodology Drafting Feature evaluation and selection Information Technology and Systems Integrated circuits Machine learning algorithms Optimization Pattern Recognition Prediction algorithms latent structure Feature selection nonnegative spectral clustering row-sparsity
ISSN:	1041-4347, 1558-2191
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