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## Locality Preserving Projection," (2004)

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Venue: | Neural Information Processing System, |

Citations: | 414 - 16 self |

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Citation Context ...th (linked by solid line), illustrating one particular mode of variability in pose. 4.4. Face Recognition PCA and LDA are the two most widely used subspace learning techniques for face recognition [1]=-=[7]-=-. These methods project the training sample faces to a low dimensional representation space where the recognition is carried out. The main supposition behind this procedure is that the face space (giv... |

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Citation Context ...incipal direction obtained by PCA, while they are well separated in the principal direction obtained by LPP. 4.2. 2-D Data Visulization An experiment was conducted with the Multiple Features Database =-=[3]-=-. This dataset consists of features of handwritten numbers (`0'-`9') extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in bi... |

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Citation Context ...perties (1) and (2) above, we know of no other linear projective technique that has such a property. 4. LPP is defined everywhere. Recall that nonlinear dimensionality reduction techniques like ISOMAP=-=[6]-=-, LLE[5], Laplacian eigenmaps[2] are defined only on the training data points and it is unclear how to evaluate the map for new test points. In contrast, the Locality Preserving Projection may be simp... |

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Citation Context ...(1) and (2) above, we know of no other linear projective technique that has such a property. 4. LPP is defined everywhere. Recall that nonlinear dimensionality reduction techniques like ISOMAP[6], LLE=-=[5]-=-, Laplacian eigenmaps[2] are defined only on the training data points and it is unclear how to evaluate the map for new test points. In contrast, the Locality Preserving Projection may be simply appli... |

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Citation Context ... path (linked by solid line), illustrating one particular mode of variability in pose. 4.4. Face Recognition PCA and LDA are the two most widely used subspace learning techniques for face recognition =-=[1]-=-[7]. These methods project the training sample faces to a low dimensional representation space where the recognition is carried out. The main supposition behind this procedure is that the face space (... |

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Citation Context ...tion in a certain sense. The representation map generated by the algorithm may be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold =-=[2]-=-. The new algorithm is interesting from a number of perspectives. 1. The maps are designed to minimize a different objective criterion from the classical linear techniques. 2. The locality preserving ... |

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Face Database,
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(Show Context)
Citation Context ...nition of the faces can be performed in this reduced space. In this subsection, we consider the application of LPP to face recognition. The database used for this experiment is the Yale face database =-=[8]-=-. It is constructed at the Yale Center for Computational Vision and Control. It contains 165 grayscale images of 15 individuals. The images demonstrate variations in lighting condition (left-light, ce... |

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