Yuanhong Li

Bio: Yuanhong Li is an academic researcher from Wayne State University. The author has contributed to research in topics: Cluster analysis & Feature selection. The author has an hindex of 7, co-authored 12 publications receiving 370 citations. Previous affiliations of Yuanhong Li include General Motors & University of Science and Technology of China.

Enhancement effect of photoluminescence in assemblies of nano-zno particles/silica aerogels

Abstract: SiO2 aerogels were prepared by a sol-gel method and supercritical drying. Introduction of nano-ZnO into nanopores of the SiO2 aerogels is performed by immersion, chemical reaction, and hydrolysis. Photoluminescence of these assemblies of nano-ZnO particles/SiO2 aerogels were systematically measured. For each assembly, a very strong photoluminescence (PL) band with the peak position of about 500 nm was observed. The luminescence intensities of the assemblies of nano-ZnO particles/SiO2 aerogels are 10–50 times higher than that of nanostructured ZnO bulks. The quantum efficiency of the PL band for the assemblies is in the range of 0.2%–1%. This luminescence enhancement effect is caused by the increase of the singly ionized oxygen vacancies in nano-ZnO particles, which are located in nanopores of the SiO2 aerogel.

Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures

Abstract: In this paper, we propose a novel approach of simultaneous localized feature selection and model detection for unsupervised learning. In our approach, local feature saliency, together with other parameters of Gaussian mixtures, are estimated by Bayesian variational learning. Experiments performed on both synthetic and real-world data sets demonstrate that our approach is superior over both global feature selection and subspace clustering methods.

Classifiability-based omnivariate decision trees

Abstract: Top-down induction of decision trees is a simple and powerful method of pattern classification. In a decision tree, each node partitions the available patterns into two or more sets. New nodes are created to handle each of the resulting partitions and the process continues. A node is considered terminal if it satisfies some stopping criteria (for example, purity, i.e., all patterns at the node are from a single class). Decision trees may be univariate, linear multivariate, or nonlinear multivariate depending on whether a single attribute, a linear function of all the attributes, or a nonlinear function of all the attributes is used for the partitioning at each node of the decision tree. Though nonlinear multivariate decision trees are the most powerful, they are more susceptible to the risks of overfitting. In this paper, we propose to perform model selection at each decision node to build omnivariate decision trees. The model selection is done using a novel classifiability measure that captures the possible sources of misclassification with relative ease and is able to accurately reflect the complexity of the subproblem at each node. The proposed approach is fast and does not suffer from as high a computational burden as that incurred by typical model selection algorithms. Empirical results over 26 data sets indicate that our approach is faster and achieves better classification accuracy compared to statistical model select algorithms.

A Gaussian Mixture Model to Detect Clusters Embedded in Feature Subspace

Abstract: The goal of unsupervised learning, i.e., clustering, is to determine the intrinsic structure of unlabeled data. Feature selection for clustering improves the performance of grouping by removing irrelevant features. Typical feature selection algorithms select a common feature subset for all the clusters. Consequently, clusters embedded in different feature subspaces are not able to be identified. In this paper, we introduce a probabilistic model based on Gaussian mixture to solve this problem. Particularly, the feature relevance for an individual cluster is treated as a probability, which is represented by localized feature saliency and estimated through Expectation Maximization (EM) algorithm during the clustering process. In addition, the number of clusters is determined simultaneously by integrating a Minimum Message Length (MML) criterion. Experiments carried on both synthetic and real-world datasets illustrate the performance of the proposed approach in finding clusters embedded in feature subspace. 1. Introduction. Clustering is unsupervised classification of data objects into different groups (clusters) such that objects in one group are similar together and dis- similar from another group. Applications of data clustering are found in many fields, such as information discovering, text mining, web analysis, image grouping, medi- cal diagnosis, and bioinformatics. Many clustering algorithms have been proposed in the literature (8). Basically, they can be categorized into two groups: hierarchical or partitional. A clustering algorithm typically considers all available features of the dataset in an attempt to learn as much as possible from data. In practice, however, some features can be irrelevant, and thus hinder the clustering performance. Feature selection, which chooses the "best" feature subset for clustering, can be applied to solve this problem. Feature selection is extensively studied in supervised learning scenario (1-3), where class labels are available for judging the performance improvement contributed by a feature selection algorithm. For unsupervised learning, feature selection is a very dif- ficult problem due to the lack of class labels, and it has received extensive attention recently. The algorithm proposed in (4) measures feature similarity by an information compression index. In (5), the relevant features are detected using a distance-based entropy measure. (6) evaluates the cluster quality over different feature subsets by normalizing cluster separability or likelihood using a cross-projection method. In (7), feature saliency is defined as a probability and estimated by the Expectation Maxi- mization (EM) algorithm using Gaussian mixture models. A variational Bayesian ap-

Chemistry of Aerogels and Their Applications

Abstract: In the present review, aerogels designate dried gels with a very high relative pore volume. These are versatile materials that are synthesized in a first step by low-temperature traditional sol-gel chemistry. However, while in the final step most wet gels are often dried by evaporation to produce so-called xerogels, aerogels are dried by other techniques, essentially supercritical drying. As a result, the dry samples keep the very unusual porous texture which they had in the wet stage. In general these dry solids have very low apparent densities, large specific surface areas, and in most cases they exhibit amorphous structures when examined by X-ray diffraction (XRD) methods. In addition, they are metastable from the point of view of their thermodynamic properties. Consequently, they often undertake a structural evolution by chemical transformation, when aged in a liquid medium and/or heat treated. As aerogels combine the properties of being highly divided solids with their metastable character, they can develop very attractive physical and chemical properties not achievable by other means of low temperature soft chemical synthesis. In other words, they form a new class of solids showing sophisticated potentialities for a range of applications. These applications as well as chemical and physical aspects of these materials were regularly detailed and discussed in a series of symposia on aerogels,1-5 the last of them being held in Albuquerque in 2000.6 Reviews were also regularly published, either on both xerogels and aerogels7 or more focused on the applications of aerogels.8-13 The particularly interesting properties of aerogels arise from the extraordinary flexibility of the solgel processing, coupled with original drying techniques. The wet chemistry is not basically different for making xerogels and aerogels. As this common basis has been extensively detailed in recent books,14 it does not need to be reviewed. Compared to traditional xerogels, the originality of aerogels comes from * To whom all correspondence should be addressed. † Institut de Recherches sur la Catalyse. ‡ Laboratoire d’Application de la Chimie à l’Environnement. 4243 Chem. Rev. 2002, 102, 4243−4265

A review of feature selection methods with applications

Abstract: Feature selection (FS) methods can be used in data pre-processing to achieve efficient data reduction. This is useful for finding accurate data models. Since exhaustive search for optimal feature subset is infeasible in most cases, many search strategies have been proposed in literature. The usual applications of FS are in classification, clustering, and regression tasks. This review considers most of the commonly used FS techniques. Particular emphasis is on the application aspects. In addition to standard filter, wrapper, and embedded methods, we also provide insight into FS for recent hybrid approaches and other advanced topics.

Role of copper in the green luminescence from ZnO crystals

Abstract: Electron paramagnetic resonance (EPR), photoluminescence, and infrared optical absorption have been used to investigate a ZnO crystal before and after a thermal anneal for 1 h in air at 900 °C. The sample was an undoped high quality crystal grown by the chemical vapor transport method. In addition to shallow donor impurities, the crystal contained trace amounts of copper ions. Prior to the thermal anneal, these ions were all in the Cu+ (3d10) state and the observed luminescence at 5 K, produced by 364 nm light, consisted of a broad structureless band peaking at 500 nm. After the high-temperature anneal, the Cu2+ (3d9) EPR spectrum was observed and the luminescence had changed significantly. The emission then peaked near 510 nm and showed structure identical to that reported by Dingle [Phys. Rev. Lett. 23, 579 (1969)]. Our data reaffirm that the structured green emission in ZnO is associated with Cu2+ ions. We suggest that the unstructured green emission (observed before the high-temperature anneal) is don...