On the basis of field geological studies of the granotoids in the region, mineralogical, petrological, geochemical(including stable isotope geochemical) and isotopic geochronological investigations were carried out on...On the basis of field geological studies of the granotoids in the region, mineralogical, petrological, geochemical(including stable isotope geochemical) and isotopic geochronological investigations were carried out on selected representative granitic bodies of various types. The authors have concluded that, apart from the bodies of the orogenic granitoid series, there also exist intrusions of the anorogenic granitoid series in the region. The intrusives of the two series were formed not only in different ages and tectonic environments, but also were derived from different sources of materials. Moreover, they are associated with different mineralizations, belonging to different minerologenetic series(Cheng et al., 1983). They show distinctly a series of discriminative criteria. The problems under discussion in the present paper are of important theoretical and practical significance in the studies of granites of the orogenic belt, particularly the studies of the genesis and related metallogeny of the granites of the region.展开更多
In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original fe...In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.展开更多
文摘On the basis of field geological studies of the granotoids in the region, mineralogical, petrological, geochemical(including stable isotope geochemical) and isotopic geochronological investigations were carried out on selected representative granitic bodies of various types. The authors have concluded that, apart from the bodies of the orogenic granitoid series, there also exist intrusions of the anorogenic granitoid series in the region. The intrusives of the two series were formed not only in different ages and tectonic environments, but also were derived from different sources of materials. Moreover, they are associated with different mineralizations, belonging to different minerologenetic series(Cheng et al., 1983). They show distinctly a series of discriminative criteria. The problems under discussion in the present paper are of important theoretical and practical significance in the studies of granites of the orogenic belt, particularly the studies of the genesis and related metallogeny of the granites of the region.
文摘In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.
