Abstract
Mining high-dimensional business data is a challenging problem. This paper proposes a novel approach to address the problems including (1) the curse of dimensionality and (2) the meaningfulness of the similarity measure in the high dimension space. The solution of this study is to build a generalized multiple kernel machine (GMKM) on a low-dimensional subspace. The representative subspace is created by the locally consistent matrix factorization (an improved variation of non-negative matrix factorization). The strengths of our system are two-fold: (1) GMKM takes products of kernels-corresponding to a tensor product of feature spaces. This leads to a richer and much higher dimensional feature representation, which is powerful in identifying relevant features and their apposite kernel representation. (2) Locally consistent matrix factorization finds a compact low-dimensional representation for data, which uncovers underlying information and simultaneously respects the intrinsic geometric structure of data manifold. Our system robustly outperforms traditional multiple kernel machines, and dimensionality reduction methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 120-125 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467373364 |
| DOIs | |
| Publication status | Published - 2015 Sep 23 |
| Event | 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and the 7th IEEE International Conference on Robotics, Automation and Mechatronics, RAM 2015 - Siem Reap, Cambodia Duration: 2015 Jul 15 → 2015 Jul 17 |
Publication series
| Name | Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015 |
|---|
Other
| Other | 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and the 7th IEEE International Conference on Robotics, Automation and Mechatronics, RAM 2015 |
|---|---|
| Country | Cambodia |
| City | Siem Reap |
| Period | 15-07-15 → 15-07-17 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Control and Systems Engineering
Cite this
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Intelligent data mining systems by generalized multiple kernel machines on graph based subspace. / Huang, Shian-Chang; Wu, Tung-Kuang.
Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 120-125 7274559 (Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Intelligent data mining systems by generalized multiple kernel machines on graph based subspace
AU - Huang, Shian-Chang
AU - Wu, Tung-Kuang
PY - 2015/9/23
Y1 - 2015/9/23
N2 - Mining high-dimensional business data is a challenging problem. This paper proposes a novel approach to address the problems including (1) the curse of dimensionality and (2) the meaningfulness of the similarity measure in the high dimension space. The solution of this study is to build a generalized multiple kernel machine (GMKM) on a low-dimensional subspace. The representative subspace is created by the locally consistent matrix factorization (an improved variation of non-negative matrix factorization). The strengths of our system are two-fold: (1) GMKM takes products of kernels-corresponding to a tensor product of feature spaces. This leads to a richer and much higher dimensional feature representation, which is powerful in identifying relevant features and their apposite kernel representation. (2) Locally consistent matrix factorization finds a compact low-dimensional representation for data, which uncovers underlying information and simultaneously respects the intrinsic geometric structure of data manifold. Our system robustly outperforms traditional multiple kernel machines, and dimensionality reduction methods.
AB - Mining high-dimensional business data is a challenging problem. This paper proposes a novel approach to address the problems including (1) the curse of dimensionality and (2) the meaningfulness of the similarity measure in the high dimension space. The solution of this study is to build a generalized multiple kernel machine (GMKM) on a low-dimensional subspace. The representative subspace is created by the locally consistent matrix factorization (an improved variation of non-negative matrix factorization). The strengths of our system are two-fold: (1) GMKM takes products of kernels-corresponding to a tensor product of feature spaces. This leads to a richer and much higher dimensional feature representation, which is powerful in identifying relevant features and their apposite kernel representation. (2) Locally consistent matrix factorization finds a compact low-dimensional representation for data, which uncovers underlying information and simultaneously respects the intrinsic geometric structure of data manifold. Our system robustly outperforms traditional multiple kernel machines, and dimensionality reduction methods.
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UR - http://www.scopus.com/inward/citedby.url?scp=84960877801&partnerID=8YFLogxK
U2 - 10.1109/ICCIS.2015.7274559
DO - 10.1109/ICCIS.2015.7274559
M3 - Conference contribution
T3 - Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015
SP - 120
EP - 125
BT - Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -