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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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2014-09, Vol.26 (9), p.2138-2150
Main Authors: Li, Zechao, Liu, Jing, Yang, Yi, Zhou, Xiaofang, Lu, Hanqing
Format: Article
Language:English
Subjects:
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
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Summary: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 this paper, a novel unsupervised feature selection algorithm, named clustering-guided sparse structural learning (CGSSL), is proposed by integrating cluster analysis and sparse structural analysis into a joint framework and experimentally evaluated. Nonnegative spectral clustering is developed to learn more accurate cluster labels of the input samples, which guide feature selection simultaneously. Meanwhile, the cluster labels are also predicted by exploiting the hidden structure shared by different features, which can uncover feature correlations to make the results more reliable. Row-wise sparse models are leveraged to make the proposed model suitable for feature selection. To optimize the proposed formulation, we propose an efficient iterative algorithm. Finally, extensive experiments are conducted on 12 diverse benchmarks, including face data, handwritten digit data, document data, and biomedical data. The encouraging experimental results in comparison with several representative algorithms and the theoretical analysis demonstrate the efficiency and effectiveness of the proposed algorithm for feature selection.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2013.65