Robust Unsupervised Feature Selection via Multi-Group Adaptive Graph Representation

Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method, referred to as Multi-Group Adaptive Graph Rep...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 35; no. 3; pp. 3030 - 3044
Main Authors: You, Mengbo, Yuan, Aihong, Zou, Min, He, Dongjian, Li, Xuelong
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
adaptive graph constraint
alternative iterative algorithm
Clustering
Data mining
Data models
Faces
Feature extraction
Feature selection
Graph representations
Graphical representations
Iterative algorithms
Iterative methods
Machine learning
multiple groups
Optimization
Similarity
Source code
Sparse matrices
Unsupervised feature selection
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method, referred to as Multi-Group Adaptive Graph Representation (MGAGR). Different from existing methods, the relationship between features is explored via the global similarity matrix, which is reconstructed by local similarities of multiple groups. Specifically, the similarity of a feature compared to other features can be represented by the linear combination of all the local similarities. The local similarity of a representative group is given a large weight to reconstruct the global similarity. Besides, an iterative algorithm is given to solve the optimization problem, in which the global similarity matrix, its corresponding reconstruction weights and the self-representation matrix are iteratively improved. Experimental results on 8 benchmark datasets demonstrates that the proposed method outperforms the state-of-the-art unsupervised feature selection methods in terms of clustering performance. The source code is available at: https://github.com/misteru/MGAGR .
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3124255