Abstract
Data management support to enable effective and efficient information sharing in collaborative environments is critical, especially in semantics based search and retrieval. In this paper, a novel spectrum perturbation-based subspace classification is proposed to mine semantics and other useful information from a large-scale dataset by utilizing a lower-dimensional subspace to discriminate different classes of the dataset. Among the existing subspace-based approaches, the principal component (PC) subspace is the most prevailing one and has been well studied. After investigating previous work related to PC subspace, we found that none of them had considered the perturbation on spectrum when building the subspace learning models. However, such perturbation is of certain importance and is able to provide discriminant information that helps improve classification performance by measuring the closeness of each testing data instance towards a subspace model by a closeness score based on the spectrum perturbation. Each testing data instance is assigned to its closest class by searching the smallest closeness score. Experiments are conducted to evaluate our proposed subspace classifier using data sets from three different sources, and the experimental results show that it achieves promising results and outperforms comparative subspace classifiers as well as some other commonly used classifiers.
| Original language | English |
|---|---|
| Title of host publication | ColiaborateCom 2011 - Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing |
| Pages | 67-76 |
| Number of pages | 10 |
| DOIs | |
| State | Published - Dec 1 2011 |
| Event | 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, ColiaborateCom 2011 - Orlando, FL, United States Duration: Oct 15 2011 → Oct 18 2011 |
Other
| Other | 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, ColiaborateCom 2011 |
|---|---|
| Country | United States |
| City | Orlando, FL |
| Period | 10/15/11 → 10/18/11 |
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Keywords
- classification
- closeness score
- Collaborative environment
- Principal component (PC) subspace
- spectrum perturbation
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
Cite this
Data management support via spectrum perturbation-based subspace classification in collaborative environments. / Chen, Chao; Shyu, Mei-Ling; Chen, Shu Ching.
ColiaborateCom 2011 - Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing. 2011. p. 67-76 6144790.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Data management support via spectrum perturbation-based subspace classification in collaborative environments
AU - Chen, Chao
AU - Shyu, Mei-Ling
AU - Chen, Shu Ching
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Data management support to enable effective and efficient information sharing in collaborative environments is critical, especially in semantics based search and retrieval. In this paper, a novel spectrum perturbation-based subspace classification is proposed to mine semantics and other useful information from a large-scale dataset by utilizing a lower-dimensional subspace to discriminate different classes of the dataset. Among the existing subspace-based approaches, the principal component (PC) subspace is the most prevailing one and has been well studied. After investigating previous work related to PC subspace, we found that none of them had considered the perturbation on spectrum when building the subspace learning models. However, such perturbation is of certain importance and is able to provide discriminant information that helps improve classification performance by measuring the closeness of each testing data instance towards a subspace model by a closeness score based on the spectrum perturbation. Each testing data instance is assigned to its closest class by searching the smallest closeness score. Experiments are conducted to evaluate our proposed subspace classifier using data sets from three different sources, and the experimental results show that it achieves promising results and outperforms comparative subspace classifiers as well as some other commonly used classifiers.
AB - Data management support to enable effective and efficient information sharing in collaborative environments is critical, especially in semantics based search and retrieval. In this paper, a novel spectrum perturbation-based subspace classification is proposed to mine semantics and other useful information from a large-scale dataset by utilizing a lower-dimensional subspace to discriminate different classes of the dataset. Among the existing subspace-based approaches, the principal component (PC) subspace is the most prevailing one and has been well studied. After investigating previous work related to PC subspace, we found that none of them had considered the perturbation on spectrum when building the subspace learning models. However, such perturbation is of certain importance and is able to provide discriminant information that helps improve classification performance by measuring the closeness of each testing data instance towards a subspace model by a closeness score based on the spectrum perturbation. Each testing data instance is assigned to its closest class by searching the smallest closeness score. Experiments are conducted to evaluate our proposed subspace classifier using data sets from three different sources, and the experimental results show that it achieves promising results and outperforms comparative subspace classifiers as well as some other commonly used classifiers.
KW - classification
KW - closeness score
KW - Collaborative environment
KW - Principal component (PC) subspace
KW - spectrum perturbation
UR - http://www.scopus.com/inward/record.url?scp=84863278427&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863278427&partnerID=8YFLogxK
U2 - 10.4108/icst.collaboratecom.2011.247202
DO - 10.4108/icst.collaboratecom.2011.247202
M3 - Conference contribution
AN - SCOPUS:84863278427
SN - 9781936968367
SP - 67
EP - 76
BT - ColiaborateCom 2011 - Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing
ER -