Semi-Supervised Dimensionality Reduction Algorithm of Tensor Image
Feng Zhu · 2009
Traditionally,an(n1n2) image is represented by a vector in the Euclidean space R(n1n2),thus the spatial relationships between pixels in an image are ignored.In this paper,the images are presented as points in the tensor space Rn1Rn2.Then,a semi-supervised dimensionality reduction algorithm is put forward based on pairwise constraints(must-link and cannot-link)between the images.The data in the reduced space preserve the local structure of the data manifold well.Finally,experimental results on face datasets validate the effectiveness of the proposed algorithm.