Abstract
In this paper, we develop a novel feature extractor called recursive locality preserving projection (RLPP). RLPP inherits the advantages of LPP and at the same time makes some improvements. In RLPP, two local weight graphs are constructed. By combining the ideas of LPP and FLDA, a discriminative maximum criterion is proposed to make the local within-class data pairs close and between-class data pairs apart. To further improve the algorithm performance, a simple but effective method is presented to find the statistically uncorrelated discriminative vectors one by one. In this way, each new obtained discriminative vector not only maximizes the discriminative criterion but also contains minimum redundancy. Our experimental results on five databases demonstrate that RLPP is more powerful than the related methods.
Access this article
Rent this article via DeepDyve
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig4_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig5_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig6_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig7_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-015-1745-y/MediaObjects/500_2015_1745_Fig8_HTML.jpg)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
Cai D, He X, Han J, Zhang HJ (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inform Theory 13(1):21–27
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
Foley DH, Sammon JW Jr (1975) An optimal set of discriminant vectors. IEEE Trans Comput 24(3):281–289
Guo J, Qi L, Li Y (2015) Fault detection of batch process using dynamic multi-way orthogonal locality preserving projections. J Comput Inf Syst 11(2):577–586
He X, Niyogi P (2003) Locality preserving projections. in NIPS
He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Jin Z, Yang JY, Hu ZS, Lou Z (2001a) Face recognition based on the uncorrelated discriminant transformation. Pattern Recognit 34(7):1405–1416
Jin Z, Yang JY, Tang ZM, Hu ZS (2001b) A theorem on the uncorrelated optimal discriminant vectors. Pattern Recognit 34(10):2041–2047
Jing XY, Zhang D, Jin Z (2003) Improvements on the uncorrelated optimal discriminant vectors. Pattern Recognit 36(8):1921–1923
Landgrebe DA (2003) Signal theory methods in multispectral remote sensing. Wiley, Chichester
Liang Z, Shi P (2005) Uncorrelated discriminant vectors using a kernel method. Pattern Recognit 38(2):307–310
Liang Y, Gong W, Pan Y, Li W (2005) Face recognition using uncorrelated, weighted linear discriminant analysis. Pattern recognition and image analysis. Springer, Berlin
Li F, Wang J, Tang B, Tian D (2014) Life grade recognition method based on supervised uncorrelated orthogonal locality preserving projection and K-nearest neighbor classifier. Neurocomputing 138:271–282
Martinez AM, Benavente R (1998) The AR face database. CVC technical report #24, June
Martinez AM, Benavente R (2006) The AR face database. http://rvl1.ecn.purdue.edu/aleix/~aleix_face_DB.html
Murphy PM, Aha DW (1994) UCI repository of machine learning databases. technical report, Department of Information and Computer Science, University of California, Irvine, Calif
Nie F, Xiang S, Liu Y, Hou C, Zhang C (2012) Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction. Pattern Recognit Lett 33(5):485–491
Tang B, Li F, Qin Y (2011) Fault diagnosis model based on feature compression with orthogonal locality preserving projection. Chin J Mech Eng 24(5):891–898
Wong WK, Zhao HT (2012) Supervised optimal locality preserving projection. Pattern Recognit 45(1):186–197
Xiang C, Fan XA, Lee TH (2006) Face recognition using recursive fisher linear discriminant. IEEE Trans Image Process 15(8):2097–2105
Xu Y, Yang JY, Jin Z (2003) Theory analysis on FSLDA and ULDA. Pattern Recognit 36(12):3031–3033
Yang J, Yang JY, Frangi AF, Zhang D (2003) Uncorrelated projection discriminant analysis and its application to face image feature extraction. Int J Patt Recogn Artif Intell 17(8):1325–1347
Yen S, Wu CM, Wang H (2012) A block-based orthogonal locality preserving projection method for face super-resolution. Intell Inf Datab Syst, Springer, Berlin Heidelberg, pp 253–262
Yu YL, Zhang LM (2008) Orthogonal MFA and uncorrelated MFA. Pattern Recognit Artif Intell 21(5):603–608 (in Chinese)
Zhang X, Chu D (2013) Sparse uncorrelated linear discriminant analysis. In: Proceedings of the 30th international conference on machine learning (ICML-13), pp 45–52
Zhao H, Sun S (2010) Optimal locality preserving projection. Image Processing (ICIP), 2010 17th IEEE international conference on. IEEE, pp 1861–1864
Zhao HT, Yuen PC, Yang JY (2005) Optimal subspace analysis for face recognition. Int J Pattern Recogn Artif Intell 19(3):375–393
Zhao H, Sun S, Jing Z (2006) Local information based uncorrelated feature extraction. Opt Eng 45(2):020505
Zheng WM, Zhao L, Zou CR (2004) An efficient algorithm to solve the small sample size problem for LDA. Pattern Recognit 37(5):1077–1079
Zheng ZL, Yang F, Tan WA, Jia J, Yang J (2007) Gabor feature-based face recognition using supervised locality preserving projections. J Signal Proc 87(10):2473–2483
Acknowledgments
This work was partially supported by the National Nature Science Foundation of China under Grant No. 61305036 and the China Postdoctoral Science Foundation funded project 2014M560657.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Xu, J., Xie, S. Recursive locality preserving projection for feature extraction. Soft Comput 20, 4099–4109 (2016). https://doi.org/10.1007/s00500-015-1745-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-015-1745-y