Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment by LI Bin, WAN Yuan

“…Multi-view feature selection improves the efficiency of classification,clustering and other learning tasks by fusing information from multiple views to obtain representative feature subsets.However,the features of different views that describe objects are complex and interrelated.Simply searching subset of features from original space partly solves the problem of dimension,but it barely obtains the latent structural information and association information among features.Besides,using fixed similarity matrix and projection matrix is prone to lose the correlation between different views.To solve these problems,an unsupervised multi-view feature selection algorithm based on similarity matrix learning and matrix alignment(SMLMA)is proposed.Firstly,the similarity matrix based on all views is constructed,and the consistent similarity matrix and projection matrix are obtained by mani-fold learning,to explore and reserve the structural information of data to the greatest extent.Then,the matrix alignment method is used to maximize the correlation between the similarity matrix and the kernel matrix,for the purpose of using the correlation between different views and reducing the information redundancy of feature subset.Finally,the Armijo searching method is introduced to obtain the convergence result quickly.Experimental results on four datasets(Caltech-7,NUS-WIDE-OBJ,Toy Animal and MSRC-v1)show that,compared with single view feature selection and some multi-view feature selection methods,the accuracy of SMLMA is averagely improved by about 7.54%.The proposed algorithm well retains the structural information of data and the correlation between multi-view features,and captures more high-quality features.…”
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Effects of Exposure to Environmental Factors on Exhaled Nitric Oxide Levels in Patients with Non-acute Exacerbation of Bronchial Asthma by HU Xiaofeng, CAI Guangyun, XIAO Kun, XU Pusheng, YU Chunhong, LIN Haihuan

“…There was no significant difference among the three groups in floors of residential buildings, length of residence, frequency of washing and drying bed sheets, quilt covers/pillow cases, curtain materials at home, potted plants at home, whether the trash can in the home was cleaned regularly, whether there were cockroaches at home and whether kitchen waste was disposed of the same day, whether there were plush toys at home (P>0.05) ; There were statistically significant differences in smoking history, whether family members smoked at home, allergy history, location of residence, the distance between the residence and the main road with large traffic volume, whether there were factories around the residence, and whether there were pets at home (P<0.05) . …”
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Prevalence of Helicobacter Pylori Infection and Associated Risk Factors among Chinese Children: a Meta-analysis by Wenhong LI, Ziwei LI, Na WANG, Jiaxiang YIN

“…Meta-analysis further indicated that poor living conditions〔OR (95%CI) =1.47 (1.09, 2.00) 〕, history of premastication〔OR (95%CI) =2.88 (1.80, 4.60) 〕, children〔OR (95%CI) =1.56 (1.01, 2.43) 〕or family members〔OR (95%CI) =2.23 (1.68, 2.98) 〕with gastrointestinal symptoms, gnawing fingers or toys〔OR (95%CI) =3.64 (2.10, 6.32) 〕, and use of shared bathroom utensils〔OR (95%CI) =2.54 (2.54, 5.73) 〕 were all risk factors for Helicobacter pylori infection in children. …”
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