多流形的非监督线性差分投影算法

Multi-Manifold Unsupervised Linear Differential Projection Algorithm

针对非监督线性差分投影(unsupervised linear differential projection,ULDP)在特征提取过程中存在的不足,提出了基于多流形的非监督线性差分投影(multi-manifold unsupervised linear differential projection,MULDP)算法,并将其应用于人脸识别中。MULDP首先构造出多流形局部近邻图和多流形最大全局方差,然后通过多目标最优化问题求解出嵌入在高维空间的低维流形。这种映射不仅能表示全局结构,还能表示局部结构。该算法可以得到嵌入在高维空间的低维流形,更好地实现了局部与全局结构信息的有效保持。在ORL、Yale及AR人脸库上的实验结果验证了所提算法的优越性。

To overcome the drawbacks of existing unsupervised linear differential projection (ULDP), this paper proposesa novel algorithm called multi-manifold unsupervised linear differential projection (MULDP) for face recognition. Firstly, multi-manifold local neighborhood graph and the largest global variance are constructed. Nextly, a lowdimensionalmanifold embedded in high-dimensional space is calculated through the multi-objective optimization. Thismapping can represent not only the global structure but also the local structure. MULDP can get the low-dimensionalmanifolds embedded in a high-dimensional space and maintain the local and global structural information effectively.The experimental results on the ORL, Yale andAR face databases demonstrate the superiority of the proposed algorithm.