图强化典型相关分析及在图像识别中的应用

典型相关分析(CCA)作为一种传统特征提取算法已经成功应用于模式识别领域，其旨在找到使两组模态数据间相关性最大的投影方向，但其本身为一种无监督的线性方法，无法利用数据内在的几何结构和监督信息，难以处理高维非线性数据。为此该文提出一种新的非线性特征提取算法，即图强化典型相关分析(GECCA)。该算法利用数据中的不同成分构建多个成分图，有效保留了数据间的复杂流形结构，采用概率评估的方法使用类标签信息，并通过图强化的方式将几何流形和监督信息融合嵌入到典型相关分析框架。为了对该算法进行评估，分别在人脸和手写体数字数据集上设计了针对性实验，良好的实验结果显示出该算法在图像识别中的优势。

Graph Enhanced Canonical Correlation Analysis and Its Application to Image Recognition

As a traditional feature extraction algorithm, Canonical Correlation Analysis (CCA) has been excellently used for the field of pattern recognition. It aims to find the projection direction that makes the maximum of the correlation between two groups of modal data. However, since the algorithm is an unsupervised linear method, it can not use intrinsic geometry structures and supervised information hidden in data, which will cause difficulty in dealing with high-dimensional nonlinear data. Therefore, this paper proposes a new nonlinear feature extraction algorithm, namely Graph Enhanced Canonical Correlation Analysis (GECCA). The algorithm uses different components of the data to construct multiple component graphs, which retains effectively the complex manifold structures between the data. The algorithm utilizes the probability evaluation method to use class label information, and the graph enhancement method is utilized to integrate the geometry manifolds and the supervised information into the typical correlation analysis framework. Targeted experiments are designed on the face and handwritten digital image datasets to evaluate the algorithm. Good experimental results show the advantages of GECCA in image recognition.