Computer Science ›› 2015, Vol. 42 ›› Issue (5): 94-97.doi: 10.11896/j.issn.1002-137X.2015.05.019
Previous Articles Next Articles
BAO Xing, ZHANG Li, ZHAO Meng-meng and YANG Ji-wen
[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! |
|