Abstract
Patch alignment (PA) framework provides us a useful way to obtain the explicit mapping for dimensionality reduction. Under the PA framework, we propose the marginal patch alignment (MPA) for dimensionality reduction. MPA performs the optimization from the part to the whole. In the phase of the patch optimization, the marginal between-class and within-class local neighborhoods of each training sample are selected to build the local marginal patches. By performing the patch optimization, on the one hand, the contributions of each sample for optimal subspace selection are distinguished. On the other hand, the marginal structure information is exploited to extract discriminative features such that the marginal distance between the two different categories is enlarged in the low transformed subspace. In the phase of the whole alignment, a trick is performed to unify all of the local patches into a globally linear system and make MPA obtain the whole optimization. The experimental results on the Yale face database, the UCI Wine dataset, the Yale-B face database, and the AR face database, show the effectiveness and efficiency of MPA.
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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 (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in neural information processing systems. MIT Press, Cambridge, pp 585–591
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
Bengio Y, Paiement J, Vincent P (2003) Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. in Proc. Adv. Neural Inf. Process. Syst. 177–184
Fukunaga K (1990) Statistical pattern recognition. Academic Press, New York
Fukunaga K (1991) Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York
Gonzalez RC, Woods RE (1997) Digital Image Processing. Addison Wesley
He X, Niyogi P (2003) Locality Preserving Projections. In: Proceedings of the 16th conference on neural information processing systems
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
He X, Cai D, Yan S, Zhang HJ (2005) Neighborhood preserving embedding. In Proc. Int. Conf. omputer Vision (ICCV’05)
Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698
Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157–165
Liu Q, Lu H, Ma S (2004) Improving kernel Fisher discriminant analysis for face recognition. IEEE Trans Circuits Syst Video Technol 14(1):42–49
Martinez AM, Benavente R (1998) The AR Face Database. CVC Technical Report #24
Martinez AM, Benavente R (2006) The AR face database. http://rvl1.ecn.purdue.edu/aleix/~aleix_face_DB.html
Mtiller K, Mika S, Riitsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12(2):181–201
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326
Tenenbaum JB, deSilva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323
Xu J, Yang J, Gu Z, Zhang N (2014) Median-mean line based discriminant analysis. Neurocomputing 123:233–246
Yan S, Xu D, Zhang B (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51
Yang W, Wang J, Ren M, Yang J (2009) Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recogn 42(11):2327–2334
Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn 44(8):1649–1657
Zhang T, Tao DC, Yang J (2008) Discriminative locality alignment. In: Proceedings of the 10th European Conference on Computer Vision (ECCV). Springer, Berlin, Heidelberg, pp 725–738
Zhang T, Tao DH, Li XL, Yang J (2009) Patch alignment for dimensionality reduction. IEEE Trans Knowl Data Eng 21(9):1299–1313
Zhang Z, Zha H (2004) Principle manifolds and nonlinear dimensionality reduction via local tangent space alignment. SIAM J Sci Comput 26(1):313–338
Acknowledgments
This work was partially supported by the National Nature Science Foundation of China (Grant nos. 61305036, 61322306, 61333013, and 61273192), the China Postdoctoral Science Foundation funded project (Grant 2014M560657 and 2015T80898), Scientific Funds approved in 2013 for Higher Level Talents by Guangdong Provincial universities and Project supported by GDHVPS 2014.
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Jie Xu, Shengli Xie and Wenkang Zhu, their immediate family, and any research foundation with which they are affiliated did not receive any financial payments or other benefits from any commercial entity related to the subject of this article.
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Xu, J., Xie, S. & Zhu, W. Marginal patch alignment for dimensionality reduction. Soft Comput 21, 2347–2356 (2017). https://doi.org/10.1007/s00500-015-1944-6
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DOI: https://doi.org/10.1007/s00500-015-1944-6