Research On Feature Extraction With Correlation Projection Analysis And Its Applications

Discriminant feature extraction is one of the basic problems of pattern recognition. Data can be described with fewer dimension information by feature extraction, which can improve recognition rate effectively with more steady data description. Recently, on the basis of linear discriminant feature extraction methods such as principal component analysis (PCA), linear discriminant analysis (LDA), correlation projection analysis methods such as canonical correlation analysis (CCA), partial least squares (PLS) have been used widely in many fields:data processing and analysis, regression analysis and prediction, discriminant feature extraction and fusion and so on. Especially, with the deepening understanding to the essence of correlation projection analysis, people have found the relations between CCA, PLS, PCA and LDA. Earlier study of correlation projection analysis has put the theory of discriminant feature extraction and information fusion to a new stage. The applications have also expanded to pattern classification and recognition, image compression and reconstruction, information retrieval, regression analysis, and so on. As the result of its successful application in many fields, correlation projection analysis has been one of the most lively and potential fields. On the other hand, there are many important problems from basic theory to application to be solved, which inspirit people to participate in the studies.As we know, CCA is effective means for information fusion. By the successful application of discriminant feature extraction and multi-feature fusion with CCA, it has also expanded to image's discriminant feature extraction, and the theoretic frameworks have been established.PLS regression analysis provides a regression modeling method for single/multi dependent variables with independent ones. On the course of regression modeling, original data are compressed, and interfering information (noise) is removed at the same time. PLS has solved the problems which cannot be solved with classical multivariate analysis methods, so the theoretic research of this method has developed rapidly. Its applying fields have extended to mechanics, biology, geology, medicine, sociology and economics. The robustness of PLS makes it become one of the most effective tools for regression analysis, dimension reduction and classifying technique.Our work mainly includes the following parts:(1) On the basis of other persons' work, we further perfect the theoretical framework of single/combined feature extraction and image classification & recognition with correlation projection analysis based on CCA&PLS. We discuss the principle of feature extraction and feature combine with CCA, and the capability of feature extraction with CCA&PLS under orthogonal constraints or statistical uncorrelative (conjugate orthogonal) constraints, then compare with classical methods such as PCA and LDA. The results of experiments show that correlation projection analysis is more efficient in discriminant feature extraction, especially in the case of symmetrical dual-features such as human being's dual-eyes, dual-palmprints, and we will get more ideal results.(2)On the basis of two-dimensional correlation projection analysis based on image matrices, we introduce the basic theory of 2DCCA and 2DPLS, and discuss the relationship between 2DLDA and 2DCCA. We prove that they are equivalent in c and c-1 class label encoding cases for discriminant feature extraction.(3)We analyze the defects of correlation projection analysis based on class label encoding, then introduce correlation projection analysis based on sample label encoding, and further expand it to two-dimensional cases. For the same reason, we introduce fuzzy correlation projection analysis and give new algorithms. The results of experiments show that both methods can improve the performance of feature extraction. On the basis of experiments on ORL and AR face databases, CASIA-Palmprint and PolyU Palmprint databases, combining with our practical work, we build a remote sense image database of fighters. Experiment results on it show that our algorithms are efficient and robust. Moreover it is an attempt for important targets recognition in remote sense images.(4)On the basis of regression theories and for the purpose of analysis and prediction, we use PLSR to model US presidential election in order to explore the relationship between candidates' votes and domestic economic development and other factors. As a result, we can deal with analysis and prediction with PLS successfully, and achieve preferable results. We get the purpose of "quantitating qualitative problems, and qualitatively analyzing quantitative results", and enrich the means for analysis and prediction.