Uncorrelated Locality Preserving Discriminant Analysis Based on Bionics

Imagery thinking model is an essential way of thinking for human being. It cognizes the regularity of things through various human senses, and then extracts the representative features. Therefore, using the method of imagery thinking to extract the essential characteristics of things is in conformity with the law of human cognition. According to the problem of feature extraction in face recognition technology, we propose an uncorrelated space locality preserving discriminant analysis algorithm—BULPDA based on the theory of unsupervised discriminant projection and image cognitive law. On the basis of the characteristics of human image cognitive, the proposed algorithm first builds a new construction method of similarity coefficient. Then, it applies uncorrelated space concepts to ensure the non-relevance of vector space. Finally, it gives the solution of the proposed algorithm based on singular value decomposition. The algorithm presents a new idea of feature extraction. The experimental results on the standard face database show that the proposed algorithm is better than the traditional preserving projection algorithms.