On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification
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[1] Alban Arrault,et al. Generative Topographic Mapping-Based Classification Models and Their Applicability Domain: Application to the Biopharmaceutics Drug Disposition Classification System (BDDCS) , 2013, J. Chem. Inf. Model..
[2] M E J Newman,et al. Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[3] Zhi-huan Song,et al. Distributed PCA Model for Plant-Wide Process Monitoring , 2013 .
[4] I. Jolliffe. Principal Component Analysis , 2002 .
[5] S. Qin,et al. Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .
[6] Stanley Wasserman,et al. Social Network Analysis: Methods and Applications , 1994 .
[7] John Nerbonne,et al. Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features , 2011, Comput. Speech Lang..
[8] R. Brereton,et al. One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process , 2010 .
[9] Leo H. Chiang,et al. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .
[10] Angelo Carotti,et al. QSAR and QSPR Studies of a Highly Structured Physicochemical Domain , 2006, J. Chem. Inf. Model..
[11] B. Rienties,et al. Understanding friendship and learning networks of international and host students using longitudinal Social Network Analysis , 2014 .
[12] Santo Fortunato,et al. Limits of modularity maximization in community detection , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[13] Richard D. Braatz,et al. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis , 2000 .
[14] Uwe Kruger,et al. Synthesis of T2 and Q statistics for process monitoring , 2004 .
[15] Hiromasa Kaneko,et al. Applicability domains and accuracy of prediction of soft sensor models , 2011 .
[16] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2004 .
[17] Jean-Loup Guillaume,et al. Fast unfolding of communities in large networks , 2008, 0803.0476.
[18] K. Funatsu,et al. Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection , 2014 .
[19] M E J Newman,et al. Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[20] E. F. Vogel,et al. A plant-wide industrial process control problem , 1993 .
[21] Hiromasa Kaneko,et al. Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants , 2014 .
[22] Michael Y. Hu,et al. Monitoring the Quality of a Chemical Production Process Using the Joint Estimation Method , 1995, J. Chem. Inf. Comput. Sci..
[23] Mourad Badri,et al. Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering , 2012 .
[24] Navneet K Dhand,et al. The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis. , 2011, Preventive veterinary medicine.
[25] Frank Harary,et al. Graph Theory , 2016 .
[26] Stelios Psarakis,et al. Multivariate statistical process control charts: an overview , 2007, Qual. Reliab. Eng. Int..
[27] Theodora Kourti,et al. Application of latent variable methods to process control and multivariate statistical process control in industry , 2005 .
[28] José L. Medina-Franco,et al. Visualization of Molecular Fingerprints , 2011, J. Chem. Inf. Model..
[29] Xv He-nan. Fault Diagnosis in Chemical Processes Based on WPA and WLS-SVM , 2010 .
[30] Jin Hyun Park,et al. Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .
[31] C K Yoo,et al. Disturbance detection and isolation in the activated sludge process. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.
[32] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[33] C. Yoo,et al. Nonlinear process monitoring using kernel principal component analysis , 2004 .
[34] Xiuxi Li,et al. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis , 2000 .
[35] John C. Young,et al. Multivariate Statistical Process Control , 2013 .
[36] Stefan Rannar,et al. A Novel Approach Using Hierarchical Clustering To Select Industrial Chemicals for Environmental Impact Assessment , 2010, J. Chem. Inf. Model..
[37] Christos Georgakis,et al. Disturbance detection and isolation by dynamic principal component analysis , 1995 .
[38] Hiromasa Kaneko,et al. Flour concentration prediction using GAPLS and GAWLS focused on data sampling issues and applicability domain , 2014 .
[39] Weihua Li,et al. Recursive PCA for adaptive process monitoring , 1999 .
[40] Hiromasa Kaneko,et al. Combined generative topographic mapping and graph theory unsupervised approach for nonlinear fault identification , 2015 .
[41] Dragos Horvath,et al. Chemical Data Visualization and Analysis with Incremental Generative Topographic Mapping: Big Data Challenge , 2015, J. Chem. Inf. Model..
[42] Vincent Le Guilloux,et al. Visual Characterization and Diversity Quantification of Chemical Libraries: 2. Analysis and Selection of Size-Independent, Subspace-Specific Diversity Indices , 2012, J. Chem. Inf. Model..
[43] Hua-Wei Shen,et al. Community Structure of Complex Networks , 2013, Springer Theses.
[44] Weihua Li,et al. Recursive PCA for Adaptive Process Monitoring , 1999 .