Abstract
Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classification (or regression) problem. In recent years, increasing attention has been on applying spare learning in unsupervised feature selection. Due to the lack of label information, the vast majority of these algorithms usually generate cluster labels via clustering algorithms and then formulate unsupervised feature selection as sparse learning based supervised feature selection with these generated cluster labels. In this paper, we propose a novel unsupervised feature selection algorithm EUFS, which directly embeds feature selection into a clustering algorithm via sparse learning without the transformation. The Alternating Direction Method of Multipliers is used to address the optimization problem of EUFS. Experimental results on various benchmark datasets demonstrate the effectiveness of the proposed framework EUFS.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
| Publisher | AI Access Foundation |
| Pages | 470-476 |
| Number of pages | 7 |
| Volume | 1 |
| ISBN (Print) | 9781577356998 |
| State | Published - Jun 1 2015 |
| Event | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States Duration: Jan 25 2015 → Jan 30 2015 |
Other
| Other | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 |
|---|---|
| Country | United States |
| City | Austin |
| Period | 1/25/15 → 1/30/15 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Embedded unsupervised feature selection. / Wang, Suhang; Tang, Jiliang; Liu, Huan.
Proceedings of the National Conference on Artificial Intelligence. Vol. 1 AI Access Foundation, 2015. p. 470-476.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Embedded unsupervised feature selection
AU - Wang, Suhang
AU - Tang, Jiliang
AU - Liu, Huan
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classification (or regression) problem. In recent years, increasing attention has been on applying spare learning in unsupervised feature selection. Due to the lack of label information, the vast majority of these algorithms usually generate cluster labels via clustering algorithms and then formulate unsupervised feature selection as sparse learning based supervised feature selection with these generated cluster labels. In this paper, we propose a novel unsupervised feature selection algorithm EUFS, which directly embeds feature selection into a clustering algorithm via sparse learning without the transformation. The Alternating Direction Method of Multipliers is used to address the optimization problem of EUFS. Experimental results on various benchmark datasets demonstrate the effectiveness of the proposed framework EUFS.
AB - Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classification (or regression) problem. In recent years, increasing attention has been on applying spare learning in unsupervised feature selection. Due to the lack of label information, the vast majority of these algorithms usually generate cluster labels via clustering algorithms and then formulate unsupervised feature selection as sparse learning based supervised feature selection with these generated cluster labels. In this paper, we propose a novel unsupervised feature selection algorithm EUFS, which directly embeds feature selection into a clustering algorithm via sparse learning without the transformation. The Alternating Direction Method of Multipliers is used to address the optimization problem of EUFS. Experimental results on various benchmark datasets demonstrate the effectiveness of the proposed framework EUFS.
UR - http://www.scopus.com/inward/record.url?scp=84959482669&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959482669&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84959482669
SN - 9781577356998
VL - 1
SP - 470
EP - 476
BT - Proceedings of the National Conference on Artificial Intelligence
PB - AI Access Foundation
ER -