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Author:

Yang, Jian (Yang, Jian.) | Zhong, Ning (Zhong, Ning.) | Liang, Peipeng (Liang, Peipeng.) | Wang, Jue (Wang, Jue.) | Yao, Yiyu (Yao, Yiyu.) (Scholars:姚一豫) | Lu, Shengfu (Lu, Shengfu.)

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CPCI-S

Abstract:

Brain activation detection is an important problem in fMRI data analysis. In this paper we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). By incorporating the idea of neighborhood consistency into one-class SVM, the method classifies a voxel as an activated or non-activated voxel by its neighbor weighted distance to a hyperplane in a high-dimensional kernel space. On two synthetic datasets under different SNRs, the proposed method almost has lower error rate than K-means clustering and fuzzy K-means clustering. On a real fMRI dataset, all the three algorithms can detect similar activated regions. Furthermore, the NOC-SVM is more stable than random algorithms, such as K-means clustering and fuzzy K-means clustering. These results show that the proposed NOC-SVM is a new effective method for activation detections in fMRI data.

Keyword:

Author Community:

  • [ 1 ] [Yang, Jian]Beijing Univ Technol, Int WIC Inst, Beijing 100022, Peoples R China
  • [ 2 ] [Liang, Peipeng]Beijing Univ Technol, Int WIC Inst, Beijing 100022, Peoples R China
  • [ 3 ] [Lu, Shengfu]Beijing Univ Technol, Int WIC Inst, Beijing 100022, Peoples R China
  • [ 4 ] [Yao, Yiyu]Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
  • [ 5 ] [Zhong, Ning]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma 371, Japan
  • [ 6 ] [Wang, Jue]Chinese Acad Sci, Beijing 100080, Peoples R China

Reprint Author's Address:

  • [Yang, Jian]Beijing Univ Technol, Int WIC Inst, Beijing 100022, Peoples R China

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Source :

ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS

Year: 2007

Page: 47-,

Language: English

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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