Informative Laplacian Projection
A new approach of constructing the similarity matrix for eigendecomposition on graph Laplacians is proposed. We first connect the Locality Preserving Projection method to probability density derivatives, which are then replaced by informative score vectors. This change yields a normalization factor and increases the contribution of the data pairs in low-density regions. The proposed method can be applied to both unsupervised and supervised learning. Empirical study on facial images is provided. The experiment results demonstrate that our method is advantageous for discovering statistical patterns in sparse data areas.
KeywordsFace Recognition Linear Discriminant Analysis Facial Image Locality Preserve Projection Nonlinear Dimensionality Reduction
- 8.Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol. 14, pp. 585–591 (2002)Google Scholar
- 9.Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality sensitive discriminant analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 2007, pp. 708–713 (2007)Google Scholar
- 14.Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. 74(036104) (2006)Google Scholar
- 15.Flynn, P.J., Bowyer, K.W., Phillips, P.J.: Assessment of time dependency in face recognition: An initial study. In: Audio- and Video-Based Biometric Person Authentication, pp. 44–51 (2003)Google Scholar