Abstract
Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods and has been widely used in many applications. In the last decades many LDA-based dimension reduction algorithms have been reported. Among these methods, orthogonal LDA (OLDA) is a famous one and several different implementations of OLDA have been proposed. In this paper, we propose a new and fast implementation of OLDA. Compared with the other OLDA implementations, our proposed implementation of OLDA is the fastest one when the dimensionality d is larger than the sample size n. Then, based on our proposed implementation of OLDA, we present an incremental OLDA algorithm which can accurately update the projection matrix of OLDA when new samples are added into the training set. The effectiveness of our proposed new OLDA algorithm and its incremental version are demonstrated by some real-world data sets.
Similar content being viewed by others
References
Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, Boston
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. John Wiley & Sons, New York
Jin Z, Yang JY, Hu ZS, Lou Z (2001) Face recognition based on the uncorrelated discriminant transformation. Pattern Recognit 34(7):1405–1416
Belhumeour 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
Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using LDA-based algorithms. IEEE Trans Neural Netw 14(1):195–200
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Howland P, Jeon M, Park H (2003) Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM J Matrix Anal Appl 25(1):165–179
Berry MW, Dumais ST, O’Brie GW (1995) Using linear algebra for intelligent information retrieval. SIAM Rev 37:573–595
Cai D, He X, Han J (2008) SRDA: an efficient algorithm for large scale discriminant analysis. IEEE Trans Knowl Data Eng 20(1):1–12
Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classfication of tumors using gene expression data. J Am Stat Assoc 97(457):77–87
Baldi P, Hatfield GW (2002) DNA microarrays and gene expression: from experiments to data analysis and modeling. Cambridge University Press, Cambridge
Howland P, Wang J, Park H (2006) Solving the small sample size problem in face recognition using generalized discriminant analysis. Pattern Recognit 39(2):227–287
Krzanowski WJ, Jonathan P, McCarthy WV, Thomas MR (1995) Discriminant analysis with singular covariance matrices: methods and applications to spectroscopic data. Appl Stat 44(1):101–115
Chu D, Thye GS (2010) A new and fast implementation for null space based linear discriminant analysis. Pattern Recognit 43(4):1373–1379
Chen LF, Liao HYM, Ko MT, Yu GJ (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit 33(10):1713–1726
Huang R, Liu Q, Lu H, Ma S (2002) Solving the small size problem of LDA. In: Proceedings of 16th Internatinal Conference. Pattern Recognition, IEEE Computer Society, Quebec, Canada, pp 29–32
Lu G-F, Wang Y (2012) Feature extraction using a fast null space based linear discriminant analysis algorithm. Inf Sci 193(6):72–80
Sharma A, Paliwal KK (2012) A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices. Pattern Recognit 45(6):2205–2213
Howland P, Park H (2004) Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Trans Pattern Anal Mach Intell 26(8):995–1006
Ye J, Janardan R, Park CH, Park H (2004) An optimization criterion for generalized discriminant analysis on undersampled problems. IEEE Trans Pattern Anal Mach Intell 26(8):982–994
Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483–502
Ye J, Janardan R, Li Q, Park H (2006) Feature reduction via generalized uncorrelated linear discriminant analysis. IEEE Trans Knowl Data Eng 18(10):1312–1322
Jin Z, Yang JY, Tang ZM, Hu ZS (2001) A theorem on the uncorrelated optimal discriminant vectors. Pattern Recognit 34(10):2041–2047
Ye J, Xiong T (2006) Computational and theoretical analysis of null space and orthogonal linear discriminant analysis. J Mach Learn Res 7:1183–1204
Ching W-K, Chu D, Liao L-Z, Wang X (2012) Regularized orthogonal linear discriminant analysis. Pattern Recognit 45:2719–2732
Chu D, Goh ST (2010) A new and fast orthogonal linear discriminant analysis on undersampled problems. SIAM J Sci Comput 32(4):2274–2297
Park H, Drake BL, Lee S, Park CH (2007) Fast linear discriminant analysis using QR decomposition and regularization. Department of Computer Science and Engineering, University of Minnesota, Minneaplis
Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space? Pattern Recognit 36(3):563–566
Lu G-F, Zou J, Wang Y (2012) Incremental complete LDA for face recognition. Pattern Recognit 45(7):2510–2521
Lu G-F, Zou J, Wang Y (2012) Incremental learning of complete linear discriminant analysis for face recognition. Knowl Based Syst 31(7):19–27
Wang X, Tang X (2004) Dual-space linear discriminant analysis for face recognition, In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04), Washington, USA, pp 564–569
Zheng W, Tang X (2009) Fast algorithm for updating the discriminnat vectors of dual-space LDA. IEEE Trans Inf Forensics Secur 4(3):418–427
Hamsici OC, Martinez AM (2008) Bayes optimality in linear discriminant analysis. IEEE Trans Pattern Anal Mach Intell 30(4):647–657
Ye J (2007) Least squares linear discriminant analysis, In: The twenty-fourth international conference on machine learning (ICML 2007), pp 1087–1093
Ye J, Li Q, Xiong H, Park H (2005) IDR/QR: an incremental dimension reduction algorithm via QR decomposition. IEEE Trans Knowl Data Eng 17(9):1208–1222
Ye J, Li Q, Xiong H, Park H, Janardan R, Kumar V (2004) IDR/QR: an incremental dimension reduction algorithm via QR decomposition. In: ACM SIGKDD Proceedings, pp 364–373
Lu G-F, Zou J, Wang Y (2012) Incremental learning of discriminant common vectors for feature extraction. Appl Math Comput 218(22):11269–11278
Yan J, Zhang B, Yan S, Yang Q, Li H, Chen Z, Xi W, Fan W, Ma W-Y, Cheng Q (2004) IMMC: Incremental maximum margin criterion. In: Proceedings of International conference knowledge discovery and data mining (KDD’04), ACM, Seattle, Washington, USA, pp 1–6
Yan J, Zhang B, Yan S, Liu N, Yang Q, Cheng Q, Li H, Chen Z, Ma W-Y (2006) A scalable supervised algorithm for dimensionality reduction on streaming data. Inf Sci 176(14):2042–2065
Pang S, Ozawa S, Kasabov N (2005) Incremental linear discriminant analysis for classification of data streams. IEEE Trans Syst Man Cybern Part B 35(5):905–914
Zhao H, Yuen PC (2008) Incremental linear discriminant analysis for face recognition. IEEE Trans Syst Man Cybern Part B 38(1):210–221
Kim T-K, Wong SF, Stenger B, Kittler J, Cipolla R (2007) Incremental linear discriminant analysis using sufficient spanning set approximations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07). Minneapolis, MN, pp 1–7
Kim T-K, Stenger B, Kittler J, Cipolla R (2011) Incremental linear discriminant analysis using sufficient spanning sets and its applications. Int J Comput Vis 91(2):216–232
Ross DA, Lim J, Lin R-S (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141
Golub GH, Loan CFV (1996) Matrix computations, 3rd edn. The Johns Hopkins University Press, Baltimore
Lang K (1995) Newsweeder: learning to filter Netnews. In: International Conference on Machine Learning (ICML), pp 331–339
Xiong T, Ye J, Cherkassky V (2006) Kernel uncorrelated and orthogonal discriminant analysis: a unified approach. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), pp 125–131
Acknowledgments
This research is supported by Anhui Provincial Natural Science Foundation (No. 1308085MF95), the Pre-research Foundation of NSFC of Anhui Polytechnic University (zryy1305), the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education (Grant No. 30920130122005), China Postdoctoral Science Foundation (2013M531251).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lu, GF., Zou, J. & Wang, Y. A New and Fast Implementation of Orthogonal LDA Algorithm and Its Incremental Extension. Neural Process Lett 43, 687–707 (2016). https://doi.org/10.1007/s11063-015-9441-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-015-9441-6