Abstract
Tensor Subspace analysis (TSA) is a typical tensor space analysis method, which can obtain the projection matrix by building an adjacency graph to model the local geometrical structure of the manifold, and has been successfully applied in many practical problems. But the essential neighbor graph constructed suffers from the following issues: the k-neighbor is computed by the whole image matrix, which leads to the loss of the correlative local area information, the graph cannot express the geometrical and discriminative structures of the original data space accurately. Based on a general platform of graph embedding, we proposed a refined tensor subspace analysis (RTSA), which will better express the spatial structure information of the original image matrices, preserving the corresponding locally information. Real face recognition experiments show the superiority of our proposed RTSA in comparison to TSA, also for corresponding supervised and unsupervised extensions.
Z. Chu: Project supported by the Key Technological Research Projects in Henan Province “the Research on Traffic Parameter Measurement and Multi-Vehicle Tracking Based on UAV Video” within the grant No.182102210197.
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References
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Turk, M., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriengman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Wang, W.Q., Aggarwal, V., Aeron, S.: Principal Component Analysis with Tensor Train Subspace (2018). arXiv:1803.05026
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Li, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29, 40–51 (2007)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2003)
He, X.F., Cai, D.: Partha Niyogi. Tensor Subspace Analysis, NIPS (2005)
Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, H.: Discriminant analysis with tensor representation. IEEE Int. Conf. Comput. Vis. Pattern Recog. 1, 526–532 (2005)
Lu, H.P., Plataniotis, K., Venetsanopoulos, M.P.C.A.: Multilinear principal component analysis of tensor objects. IEEE Trans. Neural Netw. 19(1), 18–39 (2008)
YALE data base. http://www.zjucadcg.cn/dengcai/Data/FaceData.html
ORL database: Cambridge University Computer Laboratory (2002). http://www.cl.cam.ac.uk/re-search/dtg/attarchive/facedatabase.html
Lou, S., zhao, X., Chuang, Y., Yu, H., Zhang, S.: Graph regularized sparsity discriinant analysis for face recognition. Neurocomputing 173, 290–297 (2016)
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Wu, C., Wang, W., Chu, Z. (2020). Refined Tensor Subspace Analysis. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 156. Springer, Singapore. https://doi.org/10.1007/978-981-13-9714-1_19
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DOI: https://doi.org/10.1007/978-981-13-9714-1_19
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