Computer Science ›› 2015, Vol. 42 ›› Issue (5): 94-97.doi: 10.11896/j.issn.1002-137X.2015.05.019

Previous Articles     Next Articles

Label Information-based Neighborhood Preserving Embedding

BAO Xing, ZHANG Li, ZHAO Meng-meng and YANG Ji-wen   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Neighborhood preserving embedding (NPE) is widely used for finding the intrinsic dimensionality of the data with high dimension.In order to make full use of the classification information of samples to get optimal features,we constructed an adjacent matrix which can separate different sub-manifolds as far as possible without destroying local geo-metry structure of the original data.By introducing the adjacent matrix,this paper proposed label information-based neighborhood preserving embedding (LINPE).Experiments on UCI data and ORL face databases were performed to test and evaluate LINPE.Experimental results demonstrate the effectiveness of LINPE.

Key words: Dimension reduction,Adjacent matrix,Label information,Face recognition

[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.Birkhuser 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!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!