概率二维主分量分析

二维主分量分析是一种直接面向图像矩阵表达方式的特征抽取与降维方法. 提出了一个基于二维主分量分析的概率模型. 首先, 通过对此产生式概率模型参数的最大似然估计得到主分量(矢量); 然后, 考虑到缺失数据问题, 利用期望最大化算法迭代估计模型参数和主分量. 混合概率二维主分量分析模型在人脸聚类问题上的应用表明概率二维主分量分析模型能作为图像矩阵的密度估计工具. 含有缺失值的人脸图像重构实验阐述了此模型及迭代算法的有效性.

Probabilistic Two-dimensional Principal Component Analysis

Two-dimensional principal component analysis(2DPCA)is an approach to feature extraction and dimen- sionality reduction for an image represented straightforward as a matrix.In this paper,a probabilistic model for 2DPCA, called P2DPCA,is proposed.First,the principal components(vectors)are derived through maximum-likelihood estima- tion of parameters in the generative probabilistic model.Then,due to dealing properly with missing data,we present an expectation-maximization(EM)algorithm for estimating the parameters of the model and principal components.The application to cluster face images using mixtures of P2DPCA models shows that P2DPCA model can be a tool for density-estimation of image matrix.Experimental results on face image reconstruction with missing data illustrate the effectiveness of the model and the EM iterative algorithm.