有监督的无参数核局部保持投影及人脸识别

针对发掘人脸图像中的高维非线性结构,将加核及构造无参数近邻图两种思想同时引入到局部保持投影算法中,在有监督的模式下,提出了一种新的有监督的无参数核局部保持投影(Parameter-less Supervised Kernel Locality Preserving Projection,PSKLPP)算法并给出了其推导过程。该算法通过将欧氏距离改为对离群数据更为鲁棒的余弦距离,构造无参数近邻图,利用核方法提取人脸图像中的非线性信息,并将其投影在一个高维非线性空间,运用局部保持投影算法得到一线性映射,有效避免了在计算相似矩阵过程中面临的复杂参数选择问题。在ORL和Yale人脸库上的仿真实验验证了所提算法的有效性。

Parameter-less Supervised Kernel Locality Preserving Projection and Face Recognition

In this paper,considering kernel and parameter-less nearest-neighbor graph,a novel method named parameter-less supervised kernel locality preserving projection algorithm which aims at discovering an embedding that preserves nonlinear information was proposed for face representation and recognition.In this algorithm,firstly,by changing the Euclidean distance to the Cosine distance which is more robust to outliner,and constructing a parameter-less nearest-neighbor graph,this algorithm uses the nonlinear kernel mapping to map the face data into an implicit feature space.And then a linear transformation is preformed to preserve locality geometric structures of the face image,which solves the difficulty of parameter selection in computing affinity matrix.Experiments based on both ORL and Yale face database demonstrate the effectiveness of the new algorithm.