Objective: This research compares the nature of 11 kinds of Chinese herbs in the caine kidney mixture, and provides a theoretical way to concentrated prescriptions. Methods: Based on the principal component analysis o...Objective: This research compares the nature of 11 kinds of Chinese herbs in the caine kidney mixture, and provides a theoretical way to concentrated prescriptions. Methods: Based on the principal component analysis of element contents in Chinese herbs, the effects of 25 chemical elements in the 11 kinds of Chinese herbs in the caine kidney mixture have been analyzed. Results: The traditional Chinese medicines of rehmannia, astragalus, mulberry, salvia miltiorrhiza, rhubarb in the caine kidney mixture play a major role in the treatment of chronic renal failure. Conclusion: The principal component analysis is very practical in the compatibility of medicines and concentrated prescriptions.展开更多
An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features...An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.展开更多
文摘Objective: This research compares the nature of 11 kinds of Chinese herbs in the caine kidney mixture, and provides a theoretical way to concentrated prescriptions. Methods: Based on the principal component analysis of element contents in Chinese herbs, the effects of 25 chemical elements in the 11 kinds of Chinese herbs in the caine kidney mixture have been analyzed. Results: The traditional Chinese medicines of rehmannia, astragalus, mulberry, salvia miltiorrhiza, rhubarb in the caine kidney mixture play a major role in the treatment of chronic renal failure. Conclusion: The principal component analysis is very practical in the compatibility of medicines and concentrated prescriptions.
文摘An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.
