Download or read book A Study of Sparsity Factors written by Idaho. Legislative Council. Committee on Sparsity Factors and published by . This book was released on 1964 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Latent Factor Analysis for High dimensional and Sparse Matrices written by Ye Yuan and published by Springer Nature. This book was released on 2022-11-15 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.
Download or read book Identification Through Sparsity in Factor Models written by Simon Freyaldenhoven and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multivariate Reduced Rank Regression written by Raja Velu and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the area of multivariate analysis, there are two broad themes that have emerged over time. The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relation ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues. Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regres sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix. This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.
Download or read book Statistical Learning with Sparsity written by Trevor Hastie and published by CRC Press. This book was released on 2015-05-07 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl
Download or read book Sparse Polynomial Approximation of High Dimensional Functions written by Ben Adcock and published by SIAM. This book was released on 2022-02-16 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over seventy years ago, Richard Bellman coined the term “the curse of dimensionality” to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of high-dimensional functions in real-world applications, have led to a lengthy, focused research effort on high-dimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book provides an in-depth treatment of one of the latest installments in this long and ongoing story: sparse polynomial approximation methods. These methods have emerged as useful tools for various high-dimensional approximation tasks arising in a range of applications in computational science and engineering. It begins with a comprehensive overview of best s-term polynomial approximation theory for holomorphic, high-dimensional functions, as well as a detailed survey of applications to parametric differential equations. It then describes methods for computing sparse polynomial approximations, focusing on least squares and compressed sensing techniques. Sparse Polynomial Approximation of High-Dimensional Functions presents the first comprehensive and unified treatment of polynomial approximation techniques that can mitigate the curse of dimensionality in high-dimensional approximation, including least squares and compressed sensing. It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code. The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book’s companion website (www.sparse-hd-book.com). This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques.
Download or read book Applied Quantitative Finance written by Wolfgang Karl Härdle and published by Springer. This book was released on 2017-08-02 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides practical solutions and introduces recent theoretical developments in risk management, pricing of credit derivatives, quantification of volatility and copula modeling. This third edition is devoted to modern risk analysis based on quantitative methods and textual analytics to meet the current challenges in banking and finance. It includes 14 new contributions and presents a comprehensive, state-of-the-art treatment of cutting-edge methods and topics, such as collateralized debt obligations, the high-frequency analysis of market liquidity, and realized volatility. The book is divided into three parts: Part 1 revisits important market risk issues, while Part 2 introduces novel concepts in credit risk and its management along with updated quantitative methods. The third part discusses the dynamics of risk management and includes risk analysis of energy markets and for cryptocurrencies. Digital assets, such as blockchain-based currencies, have become popular b ut are theoretically challenging when based on conventional methods. Among others, it introduces a modern text-mining method called dynamic topic modeling in detail and applies it to the message board of Bitcoins. The unique synthesis of theory and practice supported by computational tools is reflected not only in the selection of topics, but also in the fine balance of scientific contributions on practical implementation and theoretical concepts. This link between theory and practice offers theoreticians insights into considerations of applicability and, vice versa, provides practitioners convenient access to new techniques in quantitative finance. Hence the book will appeal both to researchers, including master and PhD students, and practitioners, such as financial engineers. The results presented in the book are fully reproducible and all quantlets needed for calculations are provided on an accompanying website. The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allows readers to reproduce the tables, pictures and calculations inside this Springer book.
Download or read book Iterative Solution of Large Sparse Systems of Equations written by Wolfgang Hackbusch and published by Springer. This book was released on 2016-06-21 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the second edition of this classic monograph, complete with four new chapters and updated references, readers will now have access to content describing and analysing classical and modern methods with emphasis on the algebraic structure of linear iteration, which is usually ignored in other literature. The necessary amount of work increases dramatically with the size of systems, so one has to search for algorithms that most efficiently and accurately solve systems of, e.g., several million equations. The choice of algorithms depends on the special properties the matrices in practice have. An important class of large systems arises from the discretization of partial differential equations. In this case, the matrices are sparse (i.e., they contain mostly zeroes) and well-suited to iterative algorithms. The first edition of this book grew out of a series of lectures given by the author at the Christian-Albrecht University of Kiel to students of mathematics. The second edition includes quite novel approaches.
Download or read book Case Studies in Bayesian Methods for Biopharmaceutical CMC written by Paul Faya and published by CRC Press. This book was released on 2022-12-15 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter). The collection of case studies covers a broad array of CMC topics, including stability analysis, analytical method development, specification setting, process development and optimization, process control, experimental design, dissolution testing, and comparability studies. The analysis of each case study includes a presentation of code and reproducible output. This book is written with an academic level aimed at practicing nonclinical biostatisticians, most of whom have graduate degrees in statistics. • First book of its kind focusing strictly on CMC Bayesian case studies • Case studies with code and output • Representation from several companies across the industry as well as academia • Authors are leading and well-known Bayesian statisticians in the CMC field • Accompanying website with code for reproducibility • Reflective of real-life industry applications/problems
Download or read book Handbook of Design and Analysis of Experiments written by Angela Dean and published by CRC Press. This book was released on 2015-06-26 with total page 946 pages. Available in PDF, EPUB and Kindle. Book excerpt: This carefully edited collection synthesizes the state of the art in the theory and applications of designed experiments and their analyses. It provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook covers many recent advances in the field, including designs for nonlinear models and algorithms applicable to a wide variety of design problems. It also explores the extensive use of experimental designs in marketing, the pharmaceutical industry, engineering and other areas.
Download or read book Research Bulletin written by National Education Association of the United States. Research Division and published by . This book was released on 1926 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book NEA Research Bulletin written by and published by . This book was released on 1926 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book High performance computing for solving large sparse systems Optical diffraction tomography as a case of study written by Gloria Ortega López and published by Universidad Almería. This book was released on 2015-04-14 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis, entitled €High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study€ investigates the computational issues related to the resolution of linear systems of equations which come from the discretization of physical models described by means of Partial Differential Equations (PDEs). These physical models are conceived for the description of the space-temporary behavior of some physical phenomena f(x, y, z, t) in terms of their variations (partial derivative) with respect to the dependent variables of the phenomena. There is a wide variety of discretization methods for PDEs. Two of the most well-known methods are the Finite Difference Method (FDM) and the Finite Element Method (FEM). Both methods result in an algebraic description of the model that can be translated into the approach of a linear system of equations of type (Ax = b), where A is a sparse matrix (a high percentage of zero elements) whose size depends on the required accuracy of the modeled phenomena. This thesis begins with the algebraic description of the model associated with the physical phenomena, and the work herein has been focused on the design of techniques and computational models that allow the resolution of these linear systems of equations. The main interest of this study is specially focused on models which require a high level of discretization and usually generate sparse matrices, A, which have a highly sparse structure and large size. Literature characterizes these types of problems by their high demanding computational requirements (because of their fine degree of discretization) and the sparsity of the matrices involved, suggesting that these kinds of problems can only be solved using High Performance Computing techniques and architectures. One of the main goals of this thesis is the research of the possible alternatives which allow the implementation of routines to solve large and sparse linear systems of equations using High Performance Computing (HPC). The use of massively parallel platforms (GPUs) allows the acceleration of these routines, because they have several advantages for vectorial computation schemes. On the other hand, the use of distributed memory platforms allows the resolution of problems defined by matrices of enormous size. Finally, the combination of both techniques, distributed computation and multi-GPUs, will allow faster resolution of interesting problems in which large and sparse matrices are involved. In this line, one of the goals of this thesis is to supply the scientific community with implementations based on multi-GPU clusters to solve sparse linear systems of equations, which are the key in many scientific computations. The second part of this thesis is focused on a real physical problem of Optical Diffractional Tomography (ODT) based on holographic information. ODT is a non-damaging technique which allows the extraction of the shapes of objects with high accuracy. Therefore, this technique is very suitable to the in vivo study of real specimens, microorganisms, etc., and it also makes the investigation of their dynamics possible. A preliminary physical model based on a bidimensional reconstruction of the seeding particle distribution in fluids was proposed by J. Lobera and J.M. Coupland. However, its high computational cost (in both memory requirements and runtime) made compulsory the use of HPC techniques to extend the implementation to a three dimensional model. In the second part of this thesis, the implementation and validation of this physical model for the case of three dimensional reconstructions is carried out. In such implementation, the resolution of large and sparse linear systems of equations is required. Thus, some of the algebraic routines developed in the first part of the thesis have been used to implement computational strategies capable of solving the problem of 3D reconstruction based on ODT.
Download or read book Deep Learning through Sparse and Low Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-11 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. - Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks - Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models - Provides tactics on how to build and apply customized deep learning models for various applications
Download or read book Circular written by United States. Office of Education and published by . This book was released on 1946 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sparse Phased Array Antennas Theory and Applications written by Ashutosh Kedar and published by Artech House. This book was released on 2022-03-31 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This ground-breaking resource gives you the background theories and know-how you need to effectively design active phased array antennas with wider bandwidth and scan volume utilizing sparse array technology. The book shows you how to incorporate aperiodic arrays and sparse arrays as a solution for overcoming the restrictions faced in conventional phased antenna designs – such as blind spots, limited scan volume, large power and cooling requirements, RF path losses, and increased complexity – while adhering to the maintenance of SWAP-C resources widely used in aerospace and defence. Packed with step-by-step information and research results unavailable in any other single source to date, the book presents new concepts and techniques that potentially can be applied to many critical defense and commercial requirements such as: radars, satcom on move, sonars, weather monitoring, 5G and 6G for mobile communication, fault and crack detection in buildings and underground pipelines, automotive anti-collisions mechanism in automobiles, mine detection, through wall imaging, and more. The book helps you to understand the fundamental antenna technology being deployed in modern systems and equips you to design problem-solving sparse array models proven by electromagnetic simulations that can reduce the cost and overall complexity of the existing systems. Numerous design studies are documented to validate the theories presented. The book takes into account the functional constraints in designing commercial and military systems while demonstrating provable techniques that are practical and achievable. This is an important resource for phased array antenna designers interested in utilizing sparse array technology with wider bandwidth and scan volume. The book’s straightforward approach and easy-to-follow language also make it accessible to students and those new to the field.
Download or read book Plan for Meeting Meteorological Observation Requirements Over Sparse Data Regions written by United States. Weather Bureau. Panel on Observations Over Sparse Data Regions and published by . This book was released on 1963 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:
