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      Learning nonlinear image manifolds by global alignment of local linear models

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          Adaptive Mixtures of Local Experts

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            Face recognition using laplacianfaces.

            We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
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              Unsupervised learning of finite mixture models

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                Author and article information

                Journal
                IEEE Transactions on Pattern Analysis and Machine Intelligence
                IEEE Trans. Pattern Anal. Mach. Intell.
                Institute of Electrical and Electronics Engineers (IEEE)
                0162-8828
                2160-9292
                August 2006
                August 2006
                : 28
                : 8
                : 1236-1250
                Article
                10.1109/TPAMI.2006.166
                e60f69a0-c6ce-465d-a5a0-a7a4646c09ab
                © 2006
                History

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