计算机科学 ›› 2015, Vol. 42 ›› Issue (5): 94-97.doi: 10.11896/j.issn.1002-137X.2015.05.019
包 兴,张 莉,赵梦梦,杨季文
BAO Xing, ZHANG Li, ZHAO Meng-meng and YANG Ji-wen
摘要： 邻域保持嵌入通常被广泛用于发现高维数据的固有内在维数。为了充分利用样本的类别信息,构建了一个具有判别信息的邻接矩阵,其可以使同类样本点更加紧凑而异类样本点更加疏远。在此基础上,提出了基于类别信息的邻域保持嵌入算法。基于类别信息的邻域保持嵌入算法在不破坏原始高维数据局部几何结构的同时,可以使处于不同子流形上的样本点尽量分开。在UCI数据集和ORL人脸数据集上的实验结果表明,基于类别信息的邻域保持嵌入算法具有较高的识别率。
| [1] Bellman R.Active control processes:A guided tour [M].Princeton University Press,1961 [2] Amador J J.Random projection and orthonormality for lossy image compression [J].Image and Vision Computing,2007,25(5):754-766 [3] Duda R O,Hart P E,Stork D G.Pattern classification [M].John Wiley & Sons,2012 [4] Dzwinel W,Blasiak J.Method of particles in visual clustering of multi-dimensional and large data sets [J].Future Generation Computer Systems,1999,15(3):365-379 [5] Novak E,Ritter K.The curse of dimension and a universalmethod for numerical integration [M]∥Multivariate approximation and splines.Birkhuser Basel,1997:177-187 [6] Hyvarinen A.Survey on independent component analysis [J].Neural Computing Surveys,1999,2(4):94-128 [7] Murase H,Nayar S K.Visual learning and recognition of 3-D objects from appearance [J].International Journal of Computer Vision,1995,14(1):5-24 [8] Chang Y L,Han C C,Jou F D,et al.A modular eigen subspace scheme for high-dimensional data classification [J].Future Genera-tion Computer Systems,2004,20(7):1131-1143 [9] Zhao W,Chellappa R,Phillips P J,et al.Face recognition:A literature survey [J].Acm Computing Surveys (CSUR),2003,35(4):399-458 [10] Yang J,Zhang D,Frangi A F,et al.Two-dimensional PCA:anew approach to appearance-based face representation and re-cognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):131-137 [11] Jolliffe I.Principal component analysis [M].John Wiley &Sons,Ltd,2005 [12] Belhumeur P N,Hespanha J P,Kriegman D.Eigenfaces vs.fisherfaces:Recognition using class specific linear projection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720 [13] Belkin M,Niyogi P.Laplacian eigenmaps for dimensionality reduction and data representation [J].Neural Computation,2003,15(6):1373-1396 [14] Roweis S T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding [J].Science,2000,290(5500):2323-2326 [15] Li B,Zheng C H,Huang D S.Locally linear discriminant embedding:An efficient method for face recognition [J].Pattern Reco-gnition,2008,41(12):3813-3821 [16] He X F,Partha N.Locality Preserving Projections[C]∥Proceedings of the 17th Annual Conference on Neural Information Processing Systems.Vancouver,2003:153-160 [17] He X,Cai D,Yan S,et al.Neighborhood preserving embedding[C]∥Tenth IEEE International Conference on Computer Vision,2005(ICCV 2005).IEEE,2005,2:1208-1213 [18] 杜海顺,柴秀丽,汪凤泉,等.一种邻域保持判别嵌入人脸识别方法[J].仪器仪表学报,2010,31(3):625-629 [19] Zhang W,Xue X,Lu H,et al.Discriminant neighborhood embedding for classification [J].Pattern Recognition,2006,39(11):2240-2243 [20] Kifer D,Ben-David S,Gehrke J.Detecting change in datastreams[C]∥Proceedings of the Thirtieth international conference on Very large data bases-Volume 30.VLDB Endowment,2004:180-191 [21] Samaria F S,Harter A C.Parameterisation of a stochastic model for human face identification[C]∥Proceedings of the Second IEEE Workshop on Applications of Computer Vision,1994.IEEE,1994:138-142 |
| No related articles found! |
|
||
首页