Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection

Unsupervised feature selection always occupies a key position as a preprocessing in the tasks of classification or clustering due to the existence of extra essential features within high-dimensional data. Although lots of efforts have been made, the existing methods neglect to consider the redundanc...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2019-05, Vol.30 (5), p.1587-1595
Main Authors: Li, Xuelong, Zhang, Han, Zhang, Rui, Liu, Yun, Nie, Feiping
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Constraint modelling
Data models
Economic models
Entropy
Feature extraction
Generalized uncorrelated constraint
Iterative methods
Machine learning
Manifolds
Manifolds (mathematics)
Maximum entropy
Optimization
Redundancy
regression model
Regression models
Regularization
Robustness
Task analysis
unsupervised feature selection
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Summary:Unsupervised feature selection always occupies a key position as a preprocessing in the tasks of classification or clustering due to the existence of extra essential features within high-dimensional data. Although lots of efforts have been made, the existing methods neglect to consider the redundancy of features, and thus select redundant features. In this brief, by virtue of a generalized uncorrelated constraint, we present an improved sparse regression model [generalized uncorrelated regression model (GURM)] for seeking the uncorrelated yet discriminative features. Benefited from this, the structure of data is kept in the Stiefel manifold, which avoids the potential trivial solution triggered by a conventional ridge regression model. Besides that, the uncorrelated constraint equips the model with the closed-form solution. In addition, we also incorporate a graph regularization term based on the principle of maximum entropy into the GURM model (URAFS), so as to embed the local geometric structure of data into the manifold learning. An efficient algorithm is designed to perform URAFS by virtue of the existing generalized powered iteration method. Extensive experiments on eight benchmark data sets among seven state-of-the-art methods on the task of clustering are conducted to verify the effectiveness and superiority of the proposed method.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2018.2868847