Feature Extraction Algorithm Based on Two-dimensional Discriminant Locality Alignment
Xiangqun Zhang, Xu Zhang · Jisuanji gongcheng · 2013
Anew subspace learning algorithm,which is called Two-dimensional Discriminant Locality Alignment(2DDLA) algorithm,is proposed for pattern classification,such as face recognition.The proposed algorithm integrates the idea of discriminant locality alignment and two-dimensional feature extraction algorithm.2DDLA operates in the following two stages: First,in part optimization stage,for each sample,it constructs local patch by seeking for the nearest neighbors.An object function is designed to preserve local discriminant information.Second,in whole alignment,the alignment trick is used to align all part optimizations to the whole optimization.The projection matrix can be obtained by solving a standard eigen-decomposition problem.Experimental results on ORL face database show that the algorithm has better superiority and robustness.