[1]张志友,周佳燕,邵海见,等.基于自适应邻域选择的局部线性嵌入算法[J].南京理工大学学报(自然科学版),2017,41(06):748.[doi:10.14177/j.cnki.32-1397n.2017.41.06.013]
 Zhang Zhiyou,Zhou Jiayan,Shao Haijian,et al.Locally linear embedding algorithm based on adaptiveneighborhood selection[J].Journal of Nanjing University of Science and Technology,2017,41(06):748.[doi:10.14177/j.cnki.32-1397n.2017.41.06.013]
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基于自适应邻域选择的局部线性嵌入算法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年06期
页码:
748
栏目:
出版日期:
2017-12-31

文章信息/Info

Title:
Locally linear embedding algorithm based on adaptiveneighborhood selection
文章编号:
1005-9830(2017)06-0748-05
作者:
张志友1周佳燕2邵海见3鲍安平1
1.南京信息职业技术学院 中认新能源技术学院,江苏 南京 210023; 2.江苏省南京工程高等职业学校 电子工程系,江苏 南京 211135; 3.江苏科技大学 计算机科学与工程学院,江苏 镇江 212003
Author(s):
Zhang Zhiyou1Zhou Jiayan2Shao Haijian3Bao Anping1
1.School of CQC New Energy Technology,Nanjing College of Information Technology,Nanjing 210023,China; 2.Department of Electrical Engineering,Jiangsu Province Nanjing Engineering Vocational College,Nanjing 211135,China; 3.School of Computer Science and E
关键词:
自适应邻域选择 局部线性嵌入 稀疏矩阵 数据降维 流形算法
Keywords:
adaptive neighborhood selection locally linear embedding sparse matrices dimension reduction manifold algorithm
分类号:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2017.41.06.013
摘要:
为了提高高维数据维数约简的计算效率,基于局部邻域相关的权重与稀疏矩阵,提出了1种改进的局部线性嵌入算法。对于高维数据维数约简的信息量估计,采用了相关维数估计方法来计算一致流形信息量的上界。采用Swiss、Broken swiss、Helix、Twinpeaks和Intersect 5种经典数据集进行实验评估。实验结果显示,与局部线性嵌入算法相比,针对5种经典数据集,该文算法速度分别提高了27.60%、27.51%、27.18%、28.31%和45.28%。
Abstract:
An improved locally linear embedding(LLE)algorithm based on local neighborhood-dependent weights and sparse matrices is proposed to improve the computation efficiency of dimensionality reduction for high-dimensional data.The correlation dimension estimation method is used to estimate the intrinsic information of the dimensionality reduction in high-dimensional data and the upper bound of the uniform manifold.Five classical datasets,including Swiss,Broken swiss,Helix,Twinpeaks and Intersect,are used to assess the algorithm.The results show that,compared with that of local linear embedding algorithm,the calculation speed of this algorithm on the five datasets is improved by 27.60%,27.51%,27.18%,28.31% and 45.28% respectively.

参考文献/References:

[1] 马瑞,王家廞,宋亦旭.基于局部线性嵌入(LLE)非线性降维的多流形学习[J].清华大学学报(自然科学版),2008,48(4):582-585.
Ma Rui,Wang Jiaxin,Song Yixu.Multi-manifold learning using locally linear embedding(LLE)nonlinear dimensionality reduction[J].Journal of Tsinghua University(Science and Technology),2008,48(4):582-585.
[2]李胜,陈庆伟,胡维礼.不变流形在非完整链式系统镇定中的应用[J].南京理工大学学报,2005,29(5):505-509.
Li Sheng,Chen Qingwei,Hu Weili.Application of invariant manifolds in stabilization of nonholonomic chained form systems[J].Journal of Nanjing University of Science and Technology,2005,29(5):505-509.
[3]钟晓芳,韩之俊.利用主成分分析对多质量特性的优化设计[J].南京理工大学学报,2003,27(3):301-304.
Zhong Xiaofang,Han Zhijun.Multi-response optimization design using principal component analysis[J].Journal of Nanjing University of Science and Technology,2003,27(3):301-304.
[4]张琳梅,张雪峰.曲波变换和独立分量分析的人脸识别[J].南京理工大学学报,2017,41(1):74-79.
Zhang Linmei,Zhang Xuefeng.Face recognition based on curvelet transform and independent component analysis[J].Journal of Nanjing University of Science and Technology,2017,41(1):74-79.
[5]彭令,牛瑞卿,赵艳南,等.基于核主成分分析和粒子群优化支持向量机的滑坡位移预测[J].武汉大学学报:信息科学版,2013,38(2):148-152.
Peng Ling,Niu Ruiqing,Zhao Yannan.Prediction of landslide displacement based on KPCA and PSO-SVR[J].Geomatics and Information Science of Wuhan University,2013,38(2):148-152.
[6]Sun Bingyu,Zhang Xiaoming,Li Jiuyong,et al.Feature fusion using locally linear embedding for classification[J].IEEE Transactions on Neural Networks,2010,21(1):163-168.
[7]栗志意,何亮,张卫强,等.基于鉴别性i-vector局部距离保持映射的说话人识别[J].清华大学学报(自然科学版),2012,52(5):598-601.
Li Zhiyi,He Liang,Zhang Weiqiang,et al.Speaker recognition based on discriminant i-vector local distance preserving projection[J].Journal of Tsinghua University(Science and Technology),2012,52(5):598-601.
[8]Roweis S T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[9]Kokiopoulou E,Saad Y.Orthogonal neighborhood preserving projections:A projection-based dimensionality reduction technique[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(12):2143-2156.
[10]Van der Maaten L,Postma E O,van den Herik H J.Dimensionality reduction:A comparative review[J].Journal of Machine Learning Research,2009,10(1):66-71.
[11]Hein M,Maier M.Manifold denoising[EB/OL].http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/nips2006-denoising-final_[0].pdf,2017-11-20.
[12]Lapidus M,Frankenhuijsen M.Fractal geometry,complex dimensions and zeta functions:Geometry and spectra of fractal strings[M].New York,USA:Springer-Verlag New York Inc,2012.
[13]Roweis S,Saul L.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.

备注/Memo

备注/Memo:
收稿日期:2017-10-01 修回日期:2017-11-02
基金项目:江苏省高等学校自然科学研究项目(17KJB470002)
作者简介:张志友(1982-),男,副教授,主要研究方向:物联网、测试及仪器计量,E-mail:zhangzy@njcit.cn。
引文格式:张志友,周佳燕,邵海见,等.基于自适应邻域选择的局部线性嵌入算法[J].南京理工大学学报,2017,41(6):748-752.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-12-31