An Improvement of PCA Based on the Idea of Multi - linear of Tensor
Tensor - based multilinear approaches regard human face as the composite consequence of geometries, viewpoints and illuminations, and use tensor decomposing algebra to get factor (i. e viewpoint, illumination, person, etc) subs, paces for recognition. Based on the above idea, we proposed an improvement of PCA: traditional PCA performs SVD decomposition on face iage data: A = U.VT and recognizes human face by projecting to U, now we project to , the idea is that V represents the sample space, so we project the unknown face to U and compare it to V, then we can find the nearest point in the sample space V, which the unknown belongs to. Here we give a few experiments and the results show the method performs better.
国家科技期刊平台
NSTL
万方数据
维普
