The robust principal component analysis (RPCA) is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial hete...The robust principal component analysis (RPCA) is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses (SEELU). RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU.展开更多
Density functional theory (DFT) was used to calculate molecular descriptors (properties) for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis (PCA) and hierarchical cluster analy...Density functional theory (DFT) was used to calculate molecular descriptors (properties) for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.展开更多
In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network( KPCANet) is proposed. First, the d...In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network( KPCANet) is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network( PCANet) generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.展开更多
With 50 large-flowered Chrysanthemum varieties from germplasm nursery of Wunaoshan Forest Farm in Macheng City as research objects, 64 morphological traits were investigated by field experiments adopting randomized bl...With 50 large-flowered Chrysanthemum varieties from germplasm nursery of Wunaoshan Forest Farm in Macheng City as research objects, 64 morphological traits were investigated by field experiments adopting randomized block design. The morphological differences were observed by uniformity analysis, variability analysis, principal component analysis and cluster analysis. The result showed that the vari- able coefficients of 59 traits were greater than 15%; the contribution rate of first seven principal components reached 81.45%; and it was found by clustering analy- sis that the 50 germplasm resources could be divided into four clusters with obvious morphological differences, and plant type could be used as an index for classifica- tion.展开更多
In order to facilitate the extraction of the default mode network(DMN), reduce the data complexity of the functional magnetic resonance imaging (fMRI)and overcome the restriction of the linearity of the mixing pro...In order to facilitate the extraction of the default mode network(DMN), reduce the data complexity of the functional magnetic resonance imaging (fMRI)and overcome the restriction of the linearity of the mixing process encountered with the independent component analysis(ICA), a framework of dimensionality reduction and nonlinear transformation is proposed. First, the principal component analysis(PCA)is applied to reduce the time dimension 153 594×128 of the fMRI data to 153 594×5 for simplifying complexity computation and obtaining 95% of the information. Secondly, a new kernel-based nonlinear ICA method referred as the kernel ICA(KICA)based on the Gaussian kernel is introduced to analyze the resting-state fMRI data and extract the DMN. Experimental results show that the KICA provides a better performance for the resting-state fMRI data analysis compared with the classical ICA. Furthermore, the DMN is accurately extracted and the noise is reduced.展开更多
Viviparidae are widely distributed around the globe, but there are considerable gaps in the taxonomic record. To date, 18 species of the viviparid genus Cipangopaludina have been recorded in China, but there is substa...Viviparidae are widely distributed around the globe, but there are considerable gaps in the taxonomic record. To date, 18 species of the viviparid genus Cipangopaludina have been recorded in China, but there is substantial disagreement on the validity of this taxonomy. In this study, we described the shell and internal traits of these species to better discuss the validity of related species. We found that C. ampulliformis is synonym of C. lecythis, and C. wingatei is synonym of C. chinensis, while C. ampullacea and C. fluminalis are subspecies of C. lecythis and C. chinensis, respectively. C. dianchiensis should be paled in the genus Margarya, while C. menglaensis and C. yunnanensis belong to genus Mekongia. Totally, this leaves 11 species and 2 subspecies recorded in China. Based on whether these specimens' spiral whorl depth was longer than aperture depth, these species or subspecies can be further divided into two groups, viz. chinensis group and cathayensis group, which can be determined from one another via the ratio of spiral depth and aperture depth, vas deferens and number of secondary branches of vas deferens. Additionally, Principal Component Analysis indicated that body whorl depth, shell width, aperture width and aperture length were main variables during species of Cipangopaludina. A key to all valid Chinese Cipangopaludina species were given.展开更多
In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
Independent component analysis (ICA) is a widely used method for blind source separation (BSS). The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to colle...Independent component analysis (ICA) is a widely used method for blind source separation (BSS). The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis (PCA), the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.展开更多
Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis (PCA) and kernel principal component analysis (KPCA) based on linear dimension reduction and nonlinear ...Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis (PCA) and kernel principal component analysis (KPCA) based on linear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks. By comparing different dimension reduction effects, the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes, and its calculation result is more reasonable for containing more comprehensive information. KPCA' s calculation provides scientific basis for optimum order of robotic tasks, and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes.展开更多
基金Supported by the National Scientific Foundation of China(70873118 70821140353 )+4 种基金the Chinese Academy of Sciences(KZCX2-YW-305-2 KZCX2-YW-326-1)the Ministry of Science and Technology of China ( 2006DFB919201 2008BAC43B012008BAK47B02)~~
文摘The robust principal component analysis (RPCA) is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses (SEELU). RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU.
基金This work was supported by National Nature Science Foundation of China and China Academy of Engineering Physics (No. 10376021) Provincial National Science Foundation of He'nan (No. 2004601107).
文摘Density functional theory (DFT) was used to calculate molecular descriptors (properties) for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.
基金The National Natural Science Foundation of China(No.6120134461271312+7 种基金6140108511301074)the Research Fund for the Doctoral Program of Higher Education(No.20120092120036)the Program for Special Talents in Six Fields of Jiangsu Province(No.DZXX-031)Industry-University-Research Cooperation Project of Jiangsu Province(No.BY2014127-11)"333"Project(No.BRA2015288)High-End Foreign Experts Recruitment Program(No.GDT20153200043)Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)
文摘In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network( KPCANet) is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network( PCANet) generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
基金Supported by Central Financial Forestry Science and Technology Promotion and Demonstration Project(2014HBTG07)~~
文摘With 50 large-flowered Chrysanthemum varieties from germplasm nursery of Wunaoshan Forest Farm in Macheng City as research objects, 64 morphological traits were investigated by field experiments adopting randomized block design. The morphological differences were observed by uniformity analysis, variability analysis, principal component analysis and cluster analysis. The result showed that the vari- able coefficients of 59 traits were greater than 15%; the contribution rate of first seven principal components reached 81.45%; and it was found by clustering analy- sis that the 50 germplasm resources could be divided into four clusters with obvious morphological differences, and plant type could be used as an index for classifica- tion.
基金Key Academic Discipline during the11th Five-Year Plan Period of Jiangsu Province
文摘In order to facilitate the extraction of the default mode network(DMN), reduce the data complexity of the functional magnetic resonance imaging (fMRI)and overcome the restriction of the linearity of the mixing process encountered with the independent component analysis(ICA), a framework of dimensionality reduction and nonlinear transformation is proposed. First, the principal component analysis(PCA)is applied to reduce the time dimension 153 594×128 of the fMRI data to 153 594×5 for simplifying complexity computation and obtaining 95% of the information. Secondly, a new kernel-based nonlinear ICA method referred as the kernel ICA(KICA)based on the Gaussian kernel is introduced to analyze the resting-state fMRI data and extract the DMN. Experimental results show that the KICA provides a better performance for the resting-state fMRI data analysis compared with the classical ICA. Furthermore, the DMN is accurately extracted and the noise is reduced.
基金funded by the National Natural Science Foundation of China(31301865)the Natural Science Foundation of Zhejiang Province of China(LY12C19006)the Collection and Preparation of Display Specimens at Kunming Natural History Museum of Zoology(KSZD–EW–TZ–005)
文摘Viviparidae are widely distributed around the globe, but there are considerable gaps in the taxonomic record. To date, 18 species of the viviparid genus Cipangopaludina have been recorded in China, but there is substantial disagreement on the validity of this taxonomy. In this study, we described the shell and internal traits of these species to better discuss the validity of related species. We found that C. ampulliformis is synonym of C. lecythis, and C. wingatei is synonym of C. chinensis, while C. ampullacea and C. fluminalis are subspecies of C. lecythis and C. chinensis, respectively. C. dianchiensis should be paled in the genus Margarya, while C. menglaensis and C. yunnanensis belong to genus Mekongia. Totally, this leaves 11 species and 2 subspecies recorded in China. Based on whether these specimens' spiral whorl depth was longer than aperture depth, these species or subspecies can be further divided into two groups, viz. chinensis group and cathayensis group, which can be determined from one another via the ratio of spiral depth and aperture depth, vas deferens and number of secondary branches of vas deferens. Additionally, Principal Component Analysis indicated that body whorl depth, shell width, aperture width and aperture length were main variables during species of Cipangopaludina. A key to all valid Chinese Cipangopaludina species were given.
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
基金supported by National Natural Science Foundation(No.60875061)
文摘Independent component analysis (ICA) is a widely used method for blind source separation (BSS). The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis (PCA), the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.
基金Supported by the National Natural Science Foundation of China(No.51075005)the Beijing City Science and Technology Project(No.Z131100005313009)
文摘Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis (PCA) and kernel principal component analysis (KPCA) based on linear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks. By comparing different dimension reduction effects, the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes, and its calculation result is more reasonable for containing more comprehensive information. KPCA' s calculation provides scientific basis for optimum order of robotic tasks, and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes.