Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations

Hao Huang; Shinjae Yoo; Dantong Yu; Hong Qin

doi:10.1109/ICDM.2014.88

Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations

Hao Huang
, Shinjae Yoo
, Dantong Yu
, Hong Qin

Computer Science

Stony Brook University
Brookhaven National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Unsupervised feature selection is an important issue for high dimensional dataset analysis. However popular methods are susceptible to noisy instances (observations) or noisy features. We propose a noise-resistant feature selection algorithm by capturing multi-perspective correlations. Our proposed approach, called Noise-Resistant Unsupervised Feature Selection (NRFS), is based on multi-perspective correlation that reflects the importance of feature with respect to noise-resistant representative instances and various global trends from spectral decomposition. In this way, the model concisely captures a wide variety of local patterns. Experimental results demonstrate the effectiveness of our algorithm.

Original language	English
Title of host publication	Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
Editors	Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	210-219
Number of pages	10
Edition	January
ISBN (Electronic)	9781479943029
DOIs	https://doi.org/10.1109/ICDM.2014.88
State	Published - Jan 1 2014
Event	14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China Duration: Dec 14 2014 → Dec 17 2014

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
Number	January
Volume	2015-January
ISSN (Print)	1550-4786

Conference

Conference	14th IEEE International Conference on Data Mining, ICDM 2014
Country/Territory	China
City	Shenzhen
Period	12/14/14 → 12/17/14

Access to Document

10.1109/ICDM.2014.88

Cite this

Huang, H., Yoo, S., Yu, D., & Qin, H. (2014). Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations. In R. Kumar, H. Toivonen, J. Pei, J. Zhexue Huang, & X. Wu (Eds.), Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014 (January ed., pp. 210-219). Article 7023338 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2015-January, No. January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2014.88

Huang, Hao ; Yoo, Shinjae ; Yu, Dantong et al. / Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations. Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. editor / Ravi Kumar ; Hannu Toivonen ; Jian Pei ; Joshua Zhexue Huang ; Xindong Wu. January. ed. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 210-219 (Proceedings - IEEE International Conference on Data Mining, ICDM; January).

@inproceedings{907f693b9f1542518d5e88705eba2951,

title = "Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations",

abstract = "Unsupervised feature selection is an important issue for high dimensional dataset analysis. However popular methods are susceptible to noisy instances (observations) or noisy features. We propose a noise-resistant feature selection algorithm by capturing multi-perspective correlations. Our proposed approach, called Noise-Resistant Unsupervised Feature Selection (NRFS), is based on multi-perspective correlation that reflects the importance of feature with respect to noise-resistant representative instances and various global trends from spectral decomposition. In this way, the model concisely captures a wide variety of local patterns. Experimental results demonstrate the effectiveness of our algorithm.",

author = "Hao Huang and Shinjae Yoo and Dantong Yu and Hong Qin",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th IEEE International Conference on Data Mining, ICDM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",

year = "2014",

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doi = "10.1109/ICDM.2014.88",

language = "English",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Huang, H, Yoo, S, Yu, D & Qin, H 2014, Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations. in R Kumar, H Toivonen, J Pei, J Zhexue Huang & X Wu (eds), Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. January edn, 7023338, Proceedings - IEEE International Conference on Data Mining, ICDM, no. January, vol. 2015-January, Institute of Electrical and Electronics Engineers Inc., pp. 210-219, 14th IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, 12/14/14. https://doi.org/10.1109/ICDM.2014.88

Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations. / Huang, Hao; Yoo, Shinjae; Yu, Dantong et al.
Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. ed. / Ravi Kumar; Hannu Toivonen; Jian Pei; Joshua Zhexue Huang; Xindong Wu. January. ed. Institute of Electrical and Electronics Engineers Inc., 2014. p. 210-219 7023338 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2015-January, No. January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Huang, Hao

AU - Yoo, Shinjae

AU - Yu, Dantong

AU - Qin, Hong

PY - 2014/1/1

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N2 - Unsupervised feature selection is an important issue for high dimensional dataset analysis. However popular methods are susceptible to noisy instances (observations) or noisy features. We propose a noise-resistant feature selection algorithm by capturing multi-perspective correlations. Our proposed approach, called Noise-Resistant Unsupervised Feature Selection (NRFS), is based on multi-perspective correlation that reflects the importance of feature with respect to noise-resistant representative instances and various global trends from spectral decomposition. In this way, the model concisely captures a wide variety of local patterns. Experimental results demonstrate the effectiveness of our algorithm.

AB - Unsupervised feature selection is an important issue for high dimensional dataset analysis. However popular methods are susceptible to noisy instances (observations) or noisy features. We propose a noise-resistant feature selection algorithm by capturing multi-perspective correlations. Our proposed approach, called Noise-Resistant Unsupervised Feature Selection (NRFS), is based on multi-perspective correlation that reflects the importance of feature with respect to noise-resistant representative instances and various global trends from spectral decomposition. In this way, the model concisely captures a wide variety of local patterns. Experimental results demonstrate the effectiveness of our algorithm.

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U2 - 10.1109/ICDM.2014.88

DO - 10.1109/ICDM.2014.88

M3 - Conference contribution

AN - SCOPUS:84936932465

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 210

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BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014

A2 - Kumar, Ravi

A2 - Toivonen, Hannu

A2 - Pei, Jian

A2 - Zhexue Huang, Joshua

A2 - Wu, Xindong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE International Conference on Data Mining, ICDM 2014

Y2 - 14 December 2014 through 17 December 2014

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Huang H, Yoo S, Yu D, Qin H. Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations. In Kumar R, Toivonen H, Pei J, Zhexue Huang J, Wu X, editors, Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. January ed. Institute of Electrical and Electronics Engineers Inc. 2014. p. 210-219. 7023338. (Proceedings - IEEE International Conference on Data Mining, ICDM; January). doi: 10.1109/ICDM.2014.88

Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations

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