Dimensionality Reduction and Robustness Analysis of Large Scale Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dimensionality Reduction and Robustness Analysis of Large Scale Systems write by Rudiyanto Gunawan. This book was released on 2000. Dimensionality Reduction and Robustness Analysis of Large Scale Systems available in PDF, EPUB and Kindle.
Dimension Reduction of Large-Scale Systems
Dimension Reduction of Large-Scale Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dimension Reduction of Large-Scale Systems write by Peter Benner. This book was released on 2006-03-30. Dimension Reduction of Large-Scale Systems available in PDF, EPUB and Kindle. In the past decades, model reduction has become an ubiquitous tool in analysis and simulation of dynamical systems, control design, circuit simulation, structural dynamics, CFD, and many other disciplines dealing with complex physical models. The aim of this book is to survey some of the most successful model reduction methods in tutorial style articles and to present benchmark problems from several application areas for testing and comparing existing and new algorithms. As the discussed methods have often been developed in parallel in disconnected application areas, the intention of the mini-workshop in Oberwolfach and its proceedings is to make these ideas available to researchers and practitioners from all these different disciplines.
Robustness in Dimensionality Reduction
Robustness in Dimensionality Reduction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Robustness in Dimensionality Reduction write by Jiaxi Liang. This book was released on 2016. Robustness in Dimensionality Reduction available in PDF, EPUB and Kindle. Dimensionality reduction is widely used in many statistical applications, such as image analysis, microarray analysis, or text mining. This thesis focuses on three problems that relate to the robustness in dimension reduction. The first topic is the performance analysis in dimension reduction, that is, quantitatively assessing the performance of a algorithm on a given dataset. A criterion for success is established from the geometric point of view to address this issues. A family of goodness measures, called \textsl{local rank correlation}, is developed to assess the performance of dimensionality reduction methods. The potential application of the local rank correlation in selecting tuning parameters of dimension reduction algorithms is also explored. The second topic is the sensitivity analysis in dimension reduction. Two types of influence functions are developed as measures of robustness, based on which we develop graphical display strategies for visualizing the robustness of a dimension reduction method, and flagging potential outliers. In the third part of the thesis, a novel robust PCA framework, called \textsl{Performance-Weighted Bagging PCA}, is proposed from the perspective of model averaging. It obtains a robust linear subspace by weighted averaging a collection of subspaces produced by subsamples. The robustness against outliers is achieved by a proper weighting scheme, and possible choices of weighting scheme are investigated.
Multi-Label Dimensionality Reduction
Multi-Label Dimensionality Reduction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multi-Label Dimensionality Reduction write by Liang Sun. This book was released on 2013-11-04. Multi-Label Dimensionality Reduction available in PDF, EPUB and Kindle. Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications. Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including: How to fully exploit label correlations for effective dimensionality reduction How to scale dimensionality reduction algorithms to large-scale problems How to effectively combine dimensionality reduction with classification How to derive sparse dimensionality reduction algorithms to enhance model interpretability How to perform multi-label dimensionality reduction effectively in practical applications The authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB® package for implementing popular dimensionality reduction algorithms.
34th European Symposium on Computer Aided Process Engineering /15th International Symposium on Process Systems Engineering
34th European Symposium on Computer Aided Process Engineering /15th International Symposium on Process Systems Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook 34th European Symposium on Computer Aided Process Engineering /15th International Symposium on Process Systems Engineering write by Flavio Manenti. This book was released on 2024-06-28. 34th European Symposium on Computer Aided Process Engineering /15th International Symposium on Process Systems Engineering available in PDF, EPUB and Kindle. The 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering, contains the papers presented at the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering joint event. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. Presents findings and discussions from the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering joint event
