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基于马氏距离度量的局部线性嵌入算法

, PP. 318-324

Keywords: 局部线性嵌入,流形学习,降维,图像识别

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Abstract:

局部线性嵌入算法(LLE)中常用欧氏距离度量样本间相似度。而对于图像等高维数据,欧氏距离不能准确体现样本间的相似程度。文中提出基于马氏距离度量的局部线性嵌入算法(MLLE)。算法首先从现有样本中学习到一个马氏度量,然后在LLE算法的近邻选择、现有样本及新样本降维过程中用马氏度量作为相似性度量。将MLLE算法及其它典型的流形学习算法在ORL和USPS数据库上进行对比实验,结果表明MLLE算法具有良好的识别性能。

References

[1]  Turk M,Pentland A.Eigenfaces for Recognition.Journal of Cognitive Neuroscience,1991,3(1): 71-86
[2]  Belhumeur P N,Hespanha J P,Kriegman D J.Eigenfaces vs.Fisherfaces-Recognition Using Class Specific Linear Projection.IEEE Trans on Pattern Analysis and Machine Intelligence,1997,19(7): 711-720
[3]  Hyvarinen A.Survey on Independent Component Analysis.Neural Computing Surveys,1999,2(1): 94-128
[4]  Tenenbaum J B,de Silva V,Langford J C.A Global Geometric Framework for Nonlinear Dimensionality Reduction.Science,2000,290(5500): 2319-2323
[5]  Roweis S T,Saul L K.Nonlinear Dimensionality Reduction by Locally Linear Embedding.Science,2000,290(5500): 2323-2326
[6]  Belkin M,Niyogi P.Laplacian Eigenmaps for Dimension Reduction and Data Representation.Neural Computation,2001,15(6): 1373-1396
[7]  Zhang Zhenyue,Zha Hongyuan.Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment.Journal of Shanghai University,2004,8(4): 406-424
[8]  Donoho D L,Grimes C.Hessian Eigenmaps: New Locally Linear Embedding Techniques for High-Dimensional Data.Proc of the National Academy of Arts and Sciences,2003,100(10): 5591-5596
[9]  Wang Liwei,Zhang Yan,Feng Jufu.On the Euclidean Distance of Images.IEEE Trans on Pattern Analysis and Machine Intelligence,2005,27(8): 1334-1339
[10]  Zhang Lijing,Wang Ning.Locally Linear Embedding Based on Image Euclidean Distance // Proc of the IEEE International Conference on Automation and Logistics.Jinan,China,2007: 1914-1918
[11]  Zhou Changyin,Chen Yanqiu.Improving Nearest Neighbor Classification with Cam Weighted Distance.Pattern Recognition,2006,39(4): 635-645
[12]  Pan Yaozhang,Ge S S,Ai Mamun A A.Weighted Locally Linear Embedding for Dimension Reduction.Pattern Recognition,2009,42(5): 798-811
[13]  de Ridder D,Kouropteva O,Okun O,et al.Supervised Locally Linear Embedding // Proc of the Joint International Conference ICANN/ICONIP.Istanbul,Turkey,2003: 333-341
[14]  Zhang Shiqing.Enhanced Supervised Locally Linear Embedding.Pattern Recognition Letters,2009,30(13): 1208-1218
[15]  Chang Hong,Yeung D Y.Robust Locally Linear Embedding.Pattern Recognition,2006,39(6): 1053-1065
[16]  Hadid A,Pietikainen M.Efficient Locally Linear Embeddings of Imperfect Manifolds // Proc of the 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition.Leipzig,Germany,2003: 188-201
[17]  Ge S S,Guan Feng,Pan Yaozhang,et al.Neighborhood Linear Embedding for Intrinsic Structure Discovery.Machine Vision and Applications,2008,21(3): 391-401
[18]  Saul L K,Roweis S T.Think Globally,Fit Locally: Unsupervised Learning of Low Dimensional Manifolds.Journal of Machine Learning Research,2004,4(2): 119-155
[19]  Kouropteva O,Okun O,Matti P.Incremental Locally Linear Embedding.Pattern Recognition,2005,38(10): 1764-1767
[20]  He Xiaofei,CAI Deng,Yan Shuicheng,et al.Neighborhood Preserving Embedding // Proc of the 10th IEEE International Conference on Computer Vision.Beijing,China,2005: 1208-1213
[21]  He Xiaofei,Niyogi P.Locality Preserving Projections // Schlkope B,Platt J,Hofmann T,eds.Advances in Neural Information Processing Systems.Cambridge,USA: MIT Press,2004,XVI: 153-160
[22]  Chen Sibao,Zhao Haifei,Kong Min,et al.2D-LPP: A Two-Dimensional Extension of Locality Preserving Projections.Neurocomputing,2007,70(4/5/6): 912-921
[23]  Hu Dewen,Feng Guiyu,Zhou Zongtan.Two-Dimensional Locality Preserving Projections (2DLPP) with Its Application to Palmprint Recognition.Pattern Recognition,2007,40(1): 339-342
[24]  Pan Xub,Ruan Qiuqi.Palmprint Recognition with Improved Two-Dimensional Locality Preserving Projections.Image and Vision Computing,2008,26(9): 1261-1268
[25]  Gao Quanxue,Xu Hui,Li Yiying,et al.Two-Dimensional Supervised Local Similarity and Diversity Projection.Pattern Recognition,2010,43(10): 3359-3363
[26]  Xiang Shiming,Nie Feiping,Zhang Changshui.Learning a Mahalanobis Distance Metric for Data Clustering and Classification.Pattern Recognition,2008,41(12): 3600-3612
[27]  Varini C,Degenhard A,Nattkemper T W.ISOLLE: LLE with Geodesic Distance.Neurocomputing,2006,69(13/14/15): 1768-1771

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