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1.Computing the k-metric dimension of graphs

作者：Ismael G. Yero ; ^a ; ^{ismael.gonzalez@uca.es} ; Alejandro Estrada-Moreno^b ; ^{alejandro.estrada@urv.cat} ; Juan A. Rodrí ; guez-Velá ; zquez^b ; ^{juanalberto.rodriguez@urv.cat}

关键词：k-metric dimension ; k-metric dimensional graph ; Metric dimension ; NP-complete problem ; NP-hard problem ; Graph algorithms

刊名：Applied Mathematics and Computation

出版年：2017

2.Maximal entropy coverings and the information dimension of a complex network

作者：Eric Rosenberg ^{ericr@att.com}

关键词：Information dimension ; Box counting dimension ; Entropy ; Networks ; Fractals ; Multifractal analysis

刊名：Physics Letters A

出版年：2017

3.Canonical kernel dimension reduction

作者：Chenyang Tao^a ; ^b ; Jianfeng Feng^a ; ^b ; ^c ; ^{jianfeng64@gmail.com}

关键词：Canonical correlation analysis ; Canonical functions ; Kernel dimension reduction ; Krylov subspace ; Sufficient dimension reduction ; Reproducing kernel Hilbert space

刊名：Computational Statistics & Data Analysis

出版年：2017

4.Minors and dimension

作者：Bartosz Walczak¹ ; ^{walczak@tcs.uj.edu.pl}

关键词：Dimension ; Posets ; Cover graphs ; Structural graph theory

刊名：Journal of Combinatorial Theory, Series B

出版年：2017

5.A graph regularized dimension reduction method for out-of-sample data

作者：Mengfan Tang^aAuthor Vitae ; Feiping Nie^b ; ^{feipingnie@gmail.com}Author Vitae ; Ramesh Jain^aAuthor Vitae

关键词：Dimension reduction ; Out-of-sample data ; Graph regularized PCA ; Manifold learning ; Clustering

刊名：Neurocomputing

出版年：2017

6.A dimension reduction algorithm preserving both global and local clustering structure

作者：Weiling Cai ; ^{caiwl@nuaa.edu.cn}

关键词：Pattern recognition ; Dimension reduction ; Clustering learning ; Global structure preserving ; Local structure preserving

刊名：Knowledge-Based Systems

出版年：2017

7.Correlation dimension of financial market

作者：Chun-Xiao Nie ^{niechunxiao2009@163.com}

关键词：Dimension ; Financial market ; Time series ; Financial crisis

刊名：Physica A: Statistical Mechanics and its Applications

出版年：2017

8.Complexity of metric dimension on planar graphs

作者：Josep Diaz^a ; ^b ; ¹ ; ^{diaz@cs.upc.edu" class="auth_mail" title="E-mail the corresponding author} ; Olli Pottonen^a ; ² ; ³ ; ^{olli.pottonen@iki.fi" class="auth_mail" title="E-mail the corresponding author} ; Maria Serna^a ; ^b ; ¹ ; ^{mjserna@cs.upc.edu" class="auth_mail" title="E-mail the corresponding author} ; Erik Jan van Leeuwen^c ; ⁴ ; ^{erikjan@mpi-inf.mpg.de" class="auth_mail" title="E-mail the corresponding author}

关键词：Metric dimension ; Planar graph ; Outerplanar graph ; NP-completeness

刊名：Journal of Computer and System Sciences

出版年：2017

9.Dimension reduction estimation for probability density with data missing at random when covariables are present

作者：Jianqiu Deng ; Qihua Wang ; ^{qhwang@amss.ac.cn" class="auth_mail" title="E-mail the corresponding author}

关键词：Kernel density estimation ; Kernel regression ; Dimension reduction ; Missing at random ; Asymptotic normality

刊名：Journal of Statistical Planning and Inference

出版年：2017

10.A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction

作者：Sirisup Laohakiat ; ^{sirisup.l@gmail.com} ; Suphakant Phimoltares ; Chidchanok Lursinsap

关键词：Stream data clustering ; Dimension reduction ; Linear discriminant analysis ; Clustering algorithm ; Linear discriminant analysis subspace

刊名：Information Sciences

出版年：2017

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