The quantum calculations on five nucleic acid bases,A,G,C,U,and T,were performed by six semiempirical methods,ab initio Hartree Fork method with twenty different basis sets,and ab initio MP2 method with three differen...The quantum calculations on five nucleic acid bases,A,G,C,U,and T,were performed by six semiempirical methods,ab initio Hartree Fork method with twenty different basis sets,and ab initio MP2 method with three different basis sets.Combining with experimental geometry parameters,two steps of principal component analysis(PCA) were processed on a large amount of data obtained from calculations.According to the results of the PCAs,some important geometric parameters of the bases were selected out to represent all of the parameters to discuss the problem related to geometric structure.展开更多
The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human face recognition. Numerous algorithms tried to generalize PCA in different aspects. More recently, a technique called t...The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human face recognition. Numerous algorithms tried to generalize PCA in different aspects. More recently, a technique called two-dimensional PCA (2DPCA) was proposed to cut the computational cost of the standard PCA. Unlike PCA that treats images as vectors, 2DPCA views an image as a matrix. With a properly defined criterion, 2DPCA results in an eigenvalue problem which has a much lower dimensionality than that of PCA. In this paper, we show that 2DPCA is equivalent to a special case of an existing feature extraction method, i.e., the block-based PCA. Using the FERET database, extensive experimental results demonstrate that block-based PCA outperforms PCA on datasets that consist of relatively simple images for recognition, while PCA is more robust than 2DPCA in harder situations.展开更多
文摘The quantum calculations on five nucleic acid bases,A,G,C,U,and T,were performed by six semiempirical methods,ab initio Hartree Fork method with twenty different basis sets,and ab initio MP2 method with three different basis sets.Combining with experimental geometry parameters,two steps of principal component analysis(PCA) were processed on a large amount of data obtained from calculations.According to the results of the PCAs,some important geometric parameters of the bases were selected out to represent all of the parameters to discuss the problem related to geometric structure.
文摘The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human face recognition. Numerous algorithms tried to generalize PCA in different aspects. More recently, a technique called two-dimensional PCA (2DPCA) was proposed to cut the computational cost of the standard PCA. Unlike PCA that treats images as vectors, 2DPCA views an image as a matrix. With a properly defined criterion, 2DPCA results in an eigenvalue problem which has a much lower dimensionality than that of PCA. In this paper, we show that 2DPCA is equivalent to a special case of an existing feature extraction method, i.e., the block-based PCA. Using the FERET database, extensive experimental results demonstrate that block-based PCA outperforms PCA on datasets that consist of relatively simple images for recognition, while PCA is more robust than 2DPCA in harder situations.
