The Experts below are selected from a list of 13824 Experts worldwide ranked by ideXlab platform
Tammy A. Smecker-hane - One of the best experts on this subject based on the ideXlab platform.
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Constraints on the Origin of Manganese from the Composition of the Sagittarius Dwarf Spheroidal Galaxy and the Galactic Bulge
The Astrophysical Journal, 2003Co-Authors: Andrew Mcwilliam, R. Michael Rich, Tammy A. Smecker-haneAbstract:The trend of [Mn/Fe] in the Galactic bulge follows the solar-Neighborhood Relation, but most stars in the Sagittarius dwarf spheroidal galaxy show [Mn/Fe] deficient by approximately 0.2 dex. This leads us to conclude that the Mn yields from both type Ia and type II SNe are metallicity-dependent. Our observations militate against the idea, suggested by Gratton, that Mn is over-produced by type Ia SNe, relative to type II SNe. We predict Mn/Fe ratios, lower than the solar Neighborhood Relation, for the younger populations of nearly all dwarf galaxies, and that Mn/Fe ratios may be useful for tracing the accretion of low-mass satellites into the Milky Way.
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Constraints on the Origin of Manganese from the Composition of the Sagittarius Dwarf Spheroidal Galaxy and the Galactic Bulge
The Astrophysical Journal, 2003Co-Authors: Andrew Mcwilliam, R. Michael Rich, Tammy A. Smecker-haneAbstract:The trend of [Mn/Fe] in the Galactic bulge follows the solar Neighborhood Relation, but most stars in the Sagittarius dwarf spheroidal galaxy show [Mn/Fe] deficient by ~0.2 dex. This leads us to conclude that the Mn yields from both Type Ia and Type II supernovae (SNe) are metallicity dependent. Our observations militate against the idea, suggested by Gratton, that Mn is overproduced by Type Ia SNe, relative to Type II SNe. We predict Mn/Fe ratios, lower than the solar Neighborhood Relation, for the younger populations of nearly all dwarf galaxies, and that Mn/Fe ratios may be useful for tracing the accretion of low-mass satellites into the Milky Way.
Andrew Mcwilliam - One of the best experts on this subject based on the ideXlab platform.
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Constraints on the Origin of Manganese from the Composition of the Sagittarius Dwarf Spheroidal Galaxy and the Galactic Bulge
The Astrophysical Journal, 2003Co-Authors: Andrew Mcwilliam, R. Michael Rich, Tammy A. Smecker-haneAbstract:The trend of [Mn/Fe] in the Galactic bulge follows the solar-Neighborhood Relation, but most stars in the Sagittarius dwarf spheroidal galaxy show [Mn/Fe] deficient by approximately 0.2 dex. This leads us to conclude that the Mn yields from both type Ia and type II SNe are metallicity-dependent. Our observations militate against the idea, suggested by Gratton, that Mn is over-produced by type Ia SNe, relative to type II SNe. We predict Mn/Fe ratios, lower than the solar Neighborhood Relation, for the younger populations of nearly all dwarf galaxies, and that Mn/Fe ratios may be useful for tracing the accretion of low-mass satellites into the Milky Way.
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Constraints on the Origin of Manganese from the Composition of the Sagittarius Dwarf Spheroidal Galaxy and the Galactic Bulge
The Astrophysical Journal, 2003Co-Authors: Andrew Mcwilliam, R. Michael Rich, Tammy A. Smecker-haneAbstract:The trend of [Mn/Fe] in the Galactic bulge follows the solar Neighborhood Relation, but most stars in the Sagittarius dwarf spheroidal galaxy show [Mn/Fe] deficient by ~0.2 dex. This leads us to conclude that the Mn yields from both Type Ia and Type II supernovae (SNe) are metallicity dependent. Our observations militate against the idea, suggested by Gratton, that Mn is overproduced by Type Ia SNe, relative to Type II SNe. We predict Mn/Fe ratios, lower than the solar Neighborhood Relation, for the younger populations of nearly all dwarf galaxies, and that Mn/Fe ratios may be useful for tracing the accretion of low-mass satellites into the Milky Way.
R. Michael Rich - One of the best experts on this subject based on the ideXlab platform.
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Constraints on the Origin of Manganese from the Composition of the Sagittarius Dwarf Spheroidal Galaxy and the Galactic Bulge
The Astrophysical Journal, 2003Co-Authors: Andrew Mcwilliam, R. Michael Rich, Tammy A. Smecker-haneAbstract:The trend of [Mn/Fe] in the Galactic bulge follows the solar-Neighborhood Relation, but most stars in the Sagittarius dwarf spheroidal galaxy show [Mn/Fe] deficient by approximately 0.2 dex. This leads us to conclude that the Mn yields from both type Ia and type II SNe are metallicity-dependent. Our observations militate against the idea, suggested by Gratton, that Mn is over-produced by type Ia SNe, relative to type II SNe. We predict Mn/Fe ratios, lower than the solar Neighborhood Relation, for the younger populations of nearly all dwarf galaxies, and that Mn/Fe ratios may be useful for tracing the accretion of low-mass satellites into the Milky Way.
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Constraints on the Origin of Manganese from the Composition of the Sagittarius Dwarf Spheroidal Galaxy and the Galactic Bulge
The Astrophysical Journal, 2003Co-Authors: Andrew Mcwilliam, R. Michael Rich, Tammy A. Smecker-haneAbstract:The trend of [Mn/Fe] in the Galactic bulge follows the solar Neighborhood Relation, but most stars in the Sagittarius dwarf spheroidal galaxy show [Mn/Fe] deficient by ~0.2 dex. This leads us to conclude that the Mn yields from both Type Ia and Type II supernovae (SNe) are metallicity dependent. Our observations militate against the idea, suggested by Gratton, that Mn is overproduced by Type Ia SNe, relative to Type II SNe. We predict Mn/Fe ratios, lower than the solar Neighborhood Relation, for the younger populations of nearly all dwarf galaxies, and that Mn/Fe ratios may be useful for tracing the accretion of low-mass satellites into the Milky Way.
Yuhua Qian - One of the best experts on this subject based on the ideXlab platform.
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Accelerator for supervised Neighborhood based attribute reduction
International Journal of Approximate Reasoning, 2020Co-Authors: Zehua Jiang, Xibei Yang, Keyu Liu, Hamido Fujita, Yuhua QianAbstract:Abstract In Neighborhood rough set, radius is a key factor. Different radii may generate different Neighborhood Relations for discriminating samples. Unfortunately, it is possible that two samples with different labels are regarded as indistinguishable, mainly because the Neighborhood Relation does not always provide satisfactory discriminating performance. Moreover, it should be noticed that the process of obtaining reducts in terms of multiple different radii is very time-consuming, mainly because different radii imply different reducts and those reducts should be searched, respectively. To solve the above problems, not only a supervised Neighborhood Relation is proposed for obtaining better discriminating performance, but also an accelerator is designed to speed up the process of obtaining reducts. Firstly, both intra-class radius and inter-class radius are proposed to distinguish samples. Different from the previous approaches, the labels of samples are taken into account and then this is why our approach is referred to as the supervised Neighborhood based strategy. Secondly, from the viewpoint of the variation of radius, an accelerator is designed which aims to quickly obtain multiple radii based reducts. Such mechanism is based on the consideration that the reduct in terms of the previous radius may guide the process of obtaining the reduct in terms of the current radius. The experimental results over 12 UCI data sets show the following: 1) compared with the traditional and pseudo-label Neighborhood based reducts, our supervised Neighborhood based reducts can provide higher classification accuracies; 2) our accelerator can significantly reduce the elapsed time for obtaining reducts. This study suggests new trends for considering Neighborhood rough set related topics.
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Pseudo-label Neighborhood rough set: Measures and attribute reductions
International Journal of Approximate Reasoning, 2019Co-Authors: Xibei Yang, Shaochen Liang, Shang Gao, Yuhua QianAbstract:Abstract The scale of the radius for constructing Neighborhood Relation has a great effect on the results of Neighborhood rough sets and corresponding measures. A very small radius frequently brings us nothing because any two different samples are separated from each other, though these two samples have the same label. If the radius is growing, then there is a serious risk that samples with different labels may fall into the same Neighborhood. Obviously, the radius based Neighborhood Relation does not take the labels of samples into account, which will lead to unsatisfactory discrimination. To fill such gap, a pseudo-label strategy is systematically studied in rough set theory. Firstly, a pseudo-label Neighborhood Relation is proposed. Such Relation can differentiate samples by not only the distance but also the pseudo labels of samples. Therefore, both the Neighborhood rough set and some corresponding measures can be re-defined. Secondly, attribute reductions are explored based on the re-defined measures. The heuristic algorithm is also designed to compute reducts. Finally, the experimental results over UCI data sets tell us that our pseudo-label strategy is superior to the traditional Neighborhood approach. This is mainly because the former can significantly reduce the uncertainties and improve the classification accuracies. The Wilcoxon signed rank test results also show that Neighborhood approach and pseudo-label Neighborhood approach are so different from the viewpoints of the measures and attribute reductions in rough set theory.
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Feature Selection Based on Neighborhood Discrimination Index
IEEE Transactions on Neural Networks and Learning Systems, 2018Co-Authors: Changzhong Wang, Yuhua Qian, Qinghua Hu, Xizhao Wang, Degang Chen, Zhe DongAbstract:Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a Neighborhood discrimination index is proposed to characterize the distinguishing information of a Neighborhood Relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a Neighborhood Relation rather than Neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named Neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms.
Xiafen Zhang - One of the best experts on this subject based on the ideXlab platform.
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Subspace Clustering via Structured Sparse Relation Representation.
IEEE transactions on neural networks and learning systems, 2021Co-Authors: Lai Wei, Hao Liu, Ri-gui Zhou, Changming Zhu, Xiafen ZhangAbstract:Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of ``Relation reconstruction.'' We pointed out that a data sample could be represented by the Neighborhood Relation computed between its neighbors and itself. The Neighborhood Relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's Neighborhood Relation could be reconstructed by the Neighborhood Relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse Relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse Relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.
