Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

Differential quadrature method for space-fractional diffusion equations on 2D irregular domains

In mathematical physics, the space-fractional diffusion equations are of particular interest in the studies of physical phenomena modelled by L\'{e}vy processes, which are sometimes called super-diffusion equations. In this article, we develop the differential quadrature (DQ) methods for solving the 2D space-fractional diffusion equations on irregular domains. The methods in presence reduce the original equation into a set of ordinary differential equations (ODEs) by introducing valid DQ formulations to fractional directional derivatives based on the functional values at scattered nodal points on problem domain. The required weighted coefficients are calculated by using radial basis functions (RBFs) as trial functions, and the resultant ODEs are discretized by the Crank-Nicolson scheme. The main advantages of our methods lie in their flexibility and applicability to arbitrary domains. A series of illustrated examples are finally provided to support these points.Comment: 25 pages, 25 figures, 7 table

Comprehensive Evaluation of Endophytic Fungi and Rhizosphere Soil Fungi on the Growth of \u3cem\u3eAchnatherum inebrians\u3c/em\u3e

This study was conducted to clarify the effect of endophytic fungi and rhizosphere soil fungi on the growth of Achnatherum inebrians. In this study, the seeds of A. inebrians with endophyte-infected (EI) and endophyte-free (EF) were used as materials. Eight fungi isolated from rhizosphere soil were inoculated through germination and greenhouse pot experiment. The results showed that the endophytes, rhizosphere soil fungi and their combined effect all had significant effect on the seed germination and plant growth of A. inebrians, and the affected factors varied with the tested materials and strains. Through comprehensive evaluation of principal component analysis and subordinate function, it was found that the overall growth performance of EI was better than that of EF plants, and the strains that inhibited the growth of A. inebrians were Cladosporium. sp2 and Fusarium sp1

A convex formulation for spectral shrunk clustering

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance

Salvia miltiorrhiza treatment during early reperfusion reduced postischemic myocardial injury in the rat

Oxidative stress may play a causative role in myocardial ischemia-reperfusion injury. However, it is a relatively understudied aspect regarding an optimal timing of antioxidant intervention during ischemia-reperfusion. The present study investigates the effect of different treatment regimens of Salvia miltiorrhiza (SM) herb extracts containing phenolic compounds that possess potent antioxidant properties on postischemic myocardial functional recovery in the setting of global myocardial ischemia and reperfusion. Langendorff-perfused rat hearts were subjected to 40 min of global ischemia at 37°C followed by 60 min of reperfusion, and were randomly assigned into the untreated control and 2 SM-treated groups (n = 7 per group). In treatment 1 (SM1), 3 mg/mL of water soluble extract of SM was given for 10 min before ischemia and continued during ischemia through the aorta at a reduced flow rate of 60 μL/min, but not during reperfusion. In treatment 2 (SM2), SM (3 mg/mL) was given during the first 15 min of reperfusion. During ischemia, hearts in the control and SM2 groups were given physiological saline at 60 μL/min. The SM1 treatment reduced the production of 15-F2t- isoprostane, a specific index of oxidative stress-induced lipid peroxidation, during ischemia (94 ± 20, 43 ± 6, and 95 ± 15 pg/mL in the coronary effluent in control, SM1, and SM2 groups, respectively; p < 0.05, SM1 vs. control or SM2) and post-poned the onset of ischemic contracture. However, SM2, but not the SM1 regimen, significantly reduced 15-F 2t-isoprostane production during early reperfusion and led to optimal postischemic myocardial functional recovery (left ventricular developed pressure 51 ± 4, 46 ± 4, and 60 ± 6 mmHg in the control, SM1, and SM2 groups, respectively, at 60 min of reperfusion; p < 0.05, SM2 vs. control or SM1) and reduced myocardial infarct size as measured by triphenyltetrazolium chloride staining (26% ± 2%, 22% ± 2%, and 20% ± 2% of the total area in the control, SM1, and SM2 groups, respectively, p < 0.05, SM2 vs. control). It is concluded that S. miltiorrhiza could be beneficial in the treatment of myocardial ischemic injury and the timing of administration seems important. © 2007 NRC.published_or_final_versio

Expression analysis of four flower-specific promoters of Brassica spp. in the heterogeneous host tobacco

The 5’-flanking region of ca. 1200 bp upstream of the translation start site (TSS) of a putative cell wall protein gene was cloned from Brassica campestris, B. chinensis, B. napus and B. oleracea, and transferred to tobacco via Agrobacterium-mediation after fused to promoter-less beta-glucuronidase(GUS) reporter gene. Histochemical GUS staining and fluorometric quantification of the transgenic tobacco showed that all four promoters conferred GUS expression in petal, anther, pollen and stigma ofthe flower, not in any vegetative organs or tissues of the plants. A series of 5’-end deletion of the promoter from B. napus disclosed that the region -104 to -17 relative to TSS was sufficient to confer flower-specific expression, and the region -181 to -161 played a key role in maintaining strong drivingpower of the promoter. Besides, several enhancer and suppressor regions were also identified in the promoter