Abstract
Manifold learning approaches such as locally linear embedding algorithm (LLE) and isometric mapping (Isomap) algorithm are aimed to discover the intrinsical low dimensional variables from high-dimensional nonlinear data. While, in order to achieve effective recognition tasks based on manifold learning, many problems remain to be solved. In this paper, we propose unified algorithm based on LLE and linear discriminant analysis (ULLELDA) for those remained problems. First, training samples are mapped into low-dimensional embedding space and then LDA algorithm is used to project samples into discriminant space for enlarging between-class distances and decreasing within-class distance. Second, the unknown samples are directly mapped into discriminant space without the computation of the corresponding one in the low-dimensional embedding space. Experiments on several face databases show the advantages of the proposed algorithm.
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Zhang, J., Shen, H., Zhou, ZH. (2004). Unified Locally Linear Embedding and Linear Discriminant Analysis Algorithm (ULLELDA) for Face Recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_34
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DOI: https://doi.org/10.1007/978-3-540-30548-4_34
Publisher Name: Springer, Berlin, Heidelberg
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