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TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION 被引量：8

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION
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摘要 Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem. Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition. This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier. In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine （TWSVM） to the tensor-based method TWin Support Tensor Machines （TWSTM）, which accepts general tensors as input. To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters （MCs） detection. In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not. A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm. By comparison with TWSVM, the tensor version reduces the overfitting problem.
作者 Zhang Xinsheng Gao Xinbo Wang Ying
机构地区 VIPS Lab School of Management
出处 《Journal of Electronics(China)》 2009年第3期318-325,共8页 电子科学学刊（英文版）
基金 Supported by the National Natural Science Foundation of China (No. 60771068) the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2007F248)
关键词 检测机 双单片机 学习算法 支持向量机 模式识别 格局分析 分类问题 监督学习 Microcalcification Clusters （MCs） detection TWin Support Tensor Machine （TWSTM） TWin Support Vector Machine （TWSVM） Receiver Operating Characteristic （ROC） curve
分类号 TP3 [自动化与计算机技术—计算机科学与技术] U272.067 [机械工程—车辆工程]
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参考文献11

  • 1Dacheng Tao,Xuelong Li,Xindong Wu,Weiming Hu,Stephen J. Maybank.Supervised tensor learning[J].Knowledge and Information Systems.2007(1)
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  • 7Liyang Wei,,Yongyi Yang,Robert M.Nishikawa,and Yulei Jiang.A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications[].IEEE Transactions on Medical Imaging.2005
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同被引文献32

  • 1曾志强,高济.基于向量集约简的精简支持向量机[J].软件学报,2007,18(11):2719-2727. 被引量：15
  • 2Ding Shifei, Hua Xiaopeng. Recursive least squares projection twin support vector machines [J]. Neurocomputing, 2014, 130.. 3-9.
  • 3Ding Shifei, Jia Hongjie, Chen Jinrong, et aL Granular neural networks [J]. Artificial Intelligence Review, 2014, 41 (3): 373-384.
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  • 6Fung G, Mangasarian O L. Proximal support vector machine classifiers [C] //Proe of 2001 ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York ACM, 2001 : 77-86.
  • 7Mangasarian O L, Wild W. Multi-surface proximal support vector machine classification via generalized eigenvalues [J].IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(1): 69-74.
  • 8Jayadeva, Khemchandni R, Chandra S. Twin support vector machines for pattern classification [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29 (5): 905-910.
  • 9Cong Hanhan, Yang Chengfu, Pu Xiaorong. Efficient speaker recognition based on multi-class twin support vector machines and GMMs [C] //Proc of 2008 IEEE Conf on Robotics, Automation and Mechatronics. Piscataway, NJ.. IEEE, 2008:348-352.
  • 10Peng Xinjun. TSVR: An efficient twin support vector machine for regression [J]. Neural Networks, 2010, 23: 365-372.

引证文献8

二级引证文献19

Journal of Electronics(China)
2009年 第3期

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