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LLE Score: A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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LLE Score: A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition. / Yao, Chao; Liu, Yafeng; Jiang, Bo et al.
In: IEEE Transactions on Image Processing, Vol. 26, No. 11, 11.2017, p. 5257-5269.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Yao, C, Liu, Y, Jiang, B, Han, J & Han, J 2017, 'LLE Score: A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition', IEEE Transactions on Image Processing, vol. 26, no. 11, pp. 5257-5269.

APA

Yao, C., Liu, Y., Jiang, B., Han, J., & Han, J. (2017). LLE Score: A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition. IEEE Transactions on Image Processing, 26(11), 5257-5269.

Vancouver

Yao C, Liu Y, Jiang B, Han J, Han J. LLE Score: A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition. IEEE Transactions on Image Processing. 2017 Nov;26(11):5257-5269. Epub 2017 Jul 28.

Author

Yao, Chao ; Liu, Yafeng ; Jiang, Bo et al. / LLE Score : A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition. In: IEEE Transactions on Image Processing. 2017 ; Vol. 26, No. 11. pp. 5257-5269.

Bibtex

@article{73b051864bab415990d3d53e2ec9cc92,
title = "LLE Score: A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition",
abstract = "The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graph-preserving feature selection framework. However, we find that this straightforward application suffers from some problems. For example, it fails when the elements in the feature are all equal; it does not enjoy the property of scaling invariance and cannot capture the change of the graph efficiently. To solve these problems, we propose a new filter-based feature selection method based on LLE in this paper, which is named as LLE score. The proposed criterion measures the difference between the local structure of each feature and that of the original data. Our experiments of classification task on two face image data sets, an object image data set, and a handwriting digits data set show that LLE score outperforms state-of-the-art methods, including data variance, Laplacian score, and sparsity score.",
author = "Chao Yao and Yafeng Liu and Bo Jiang and Jungong Han and Junwei Han",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2017",
month = nov,
language = "English",
volume = "26",
pages = "5257--5269",
journal = "IEEE Transactions on Image Processing",
issn = "1057-7149",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",

}

RIS

TY - JOUR

T1 - LLE Score

T2 - A New Filter-based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition

AU - Yao, Chao

AU - Liu, Yafeng

AU - Jiang, Bo

AU - Han, Jungong

AU - Han, Junwei

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2017/11

Y1 - 2017/11

N2 - The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graph-preserving feature selection framework. However, we find that this straightforward application suffers from some problems. For example, it fails when the elements in the feature are all equal; it does not enjoy the property of scaling invariance and cannot capture the change of the graph efficiently. To solve these problems, we propose a new filter-based feature selection method based on LLE in this paper, which is named as LLE score. The proposed criterion measures the difference between the local structure of each feature and that of the original data. Our experiments of classification task on two face image data sets, an object image data set, and a handwriting digits data set show that LLE score outperforms state-of-the-art methods, including data variance, Laplacian score, and sparsity score.

AB - The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graph-preserving feature selection framework. However, we find that this straightforward application suffers from some problems. For example, it fails when the elements in the feature are all equal; it does not enjoy the property of scaling invariance and cannot capture the change of the graph efficiently. To solve these problems, we propose a new filter-based feature selection method based on LLE in this paper, which is named as LLE score. The proposed criterion measures the difference between the local structure of each feature and that of the original data. Our experiments of classification task on two face image data sets, an object image data set, and a handwriting digits data set show that LLE score outperforms state-of-the-art methods, including data variance, Laplacian score, and sparsity score.

M3 - Journal article

VL - 26

SP - 5257

EP - 5269

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

SN - 1057-7149

IS - 11

ER -