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Author : Kevin Huynh
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (143 download)
Download or read book Topics in Model Averaging and Dimensionality Reduction written by Kevin Huynh and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jiaxi Liang
Publisher :
ISBN 13 :
Total Pages : 161 pages
Book Rating : 4.:/5 (973 download)
Download or read book Robustness in Dimensionality Reduction written by Jiaxi Liang and published by . This book was released on 2016 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Gerda Claeskens
Publisher :
ISBN 13 : 9780521852258
Total Pages : 312 pages
Book Rating : 4.8/5 (522 download)
Download or read book Model Selection and Model Averaging written by Gerda Claeskens and published by . This book was released on 2008-07-28 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: First book to synthesize the research and practice from the active field of model selection.
Author : Dr. Sunil Bhutada
Publisher : Ashok Yakkaldevi
ISBN 13 : 1716688450
Total Pages : 102 pages
Book Rating : 4.7/5 (166 download)
Download or read book TOPIC MODELING USING VARIATIONS ON LATENT DIRICHLET ALLOCATION written by Dr. Sunil Bhutada and published by Ashok Yakkaldevi. This book was released on 2020-08-08 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Till date, internet has amassed an enormous number of computerized data including news, sites, website pages, eBooks, pictures, sound, video, person to person communication and different types of information, and the number is developing exponentially. Accordingly, how individuals are arranging and managing extensive data efficiently and acquiring the essential valuable information rapidly is a massive challenge. In this manner, it is important to introduce and built automatic tools which should transform huge data into valuable, knowledgeable and useful information intelligently.
Author : David Fletcher
Publisher : Springer
ISBN 13 : 3662585413
Total Pages : 107 pages
Book Rating : 4.6/5 (625 download)
Download or read book Model Averaging written by David Fletcher and published by Springer. This book was released on 2019-01-17 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
Author : Benyamin Ghojogh
Publisher : Springer Nature
ISBN 13 : 3031106024
Total Pages : 617 pages
Book Rating : 4.0/5 (311 download)
Download or read book Elements of Dimensionality Reduction and Manifold Learning written by Benyamin Ghojogh and published by Springer Nature. This book was released on 2023-02-02 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.
Author : Huaizhong Zhao
Publisher : World Scientific
ISBN 13 : 9814360910
Total Pages : 458 pages
Book Rating : 4.8/5 (143 download)
Download or read book New Trends in Stochastic Analysis and Related Topics written by Huaizhong Zhao and published by World Scientific. This book was released on 2012 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume is dedicated to Professor David Elworthy to celebrate his fundamental contribution and exceptional influence on stochastic analysis and related fields. Stochastic analysis has been profoundly developed as a vital fundamental research area in mathematics in recent decades. It has been discovered to have intrinsic connections with many other areas of mathematics such as partial differential equations, functional analysis, topology, differential geometry, dynamical systems, etc. Mathematicians developed many mathematical tools in stochastic analysis to understand and model random phenomena in physics, biology, finance, fluid, environment science, etc. This volume contains 12 comprehensive review/new articles written by world leading researchers (by invitation) and their collaborators. It covers stochastic analysis on manifolds, rough paths, Dirichlet forms, stochastic partial differential equations, stochastic dynamical systems, infinite dimensional analysis, stochastic flows, quantum stochastic analysis and stochastic Hamilton Jacobi theory. Articles contain cutting edge research methodology, results and ideas in relevant fields. They are of interest to research mathematicians and postgraduate students in stochastic analysis, probability, partial differential equations, dynamical systems, mathematical physics, as well as to physicists, financial mathematicians, engineers, etc.
Author : J. M. Bernardo
Publisher : Oxford University Press
ISBN 13 : 9780198504856
Total Pages : 886 pages
Book Rating : 4.5/5 (48 download)
Download or read book Bayesian Statistics 6 written by J. M. Bernardo and published by Oxford University Press. This book was released on 1999-08-12 with total page 886 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Author : Liang Sun
Publisher : CRC Press
ISBN 13 : 1439806152
Total Pages : 210 pages
Book Rating : 4.4/5 (398 download)
Download or read book Multi-Label Dimensionality Reduction written by Liang Sun and published by CRC Press. This book was released on 2013-11-04 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author :
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (227 download)
Download or read book Model Averaging and Dimension Selection for the Singular Value Decomposition written by and published by . This book was released on 2006 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many multivariate data analysis techniques for an m x n matrix Y are related to the model Y = M+E, where Y is an m x n matrix of full rank and M is an unobserved mean matrix of rank K
Author : Yuting Chen
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book Data Dimensionality Reduction Techniques: what Works with Machine Learning Models written by Yuting Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-dimensional data has a wide range of applications in research, such as education, health, social media, and many other research fields. However, the high dimensionality of data can raise many problems for data analyses. This study focuses on commonly used techniques of dimensionality reduction for machine learning models, which play an essential and inevitable role in data prepossessing and statistical analysis. The main issues of high-dimensional data for machine learning tasks include the accuracy of data classification and visualization in machine learning models. Therefore, in this study, machine learning algorithms are used to predict and classify datasets to evaluate the accuracy, precision, recall, and F1 score of results, which are evaluated and compared by mean, variance, confidence intervals, and coverage. This study focuses on data mining issues, comparing and discussing different dimensionality reduction techniques with different dataset features. Eight dimensionality reduction techniques (Principal Component Analysis, Kernel Principal Component Analysis, Singular Value Decomposition, Non-negative matrix factorization, Independent Component Analysis, Multidimensional Scaling, Isomap, and Auto-encoder) are compared and evaluated on simulated datasets. Specifically, this study evaluates and compares the performances of the commonly used dimensionality reduction techniques by exploring the issues about features and characteristics of different techniques through Monte Carlo simulation studies with four machine learning classification models: logistic regression, linear support vector machine, nonlinear support vector machine, and k-nearest neighbors. The results of this study indicated that the DRTs decreased the accuracy, precision, recall, and F1 scores compared with results without DRTs. And overall, MDS performed dramatically better than other DRTs. SVD, PCA, and ICA had similar results because they are all linear DRTs. Although it is also a linear DRT, NMF performed as poorly as KPCA, which is a nonlinear DRT. The other two nonlinear DRTs, Isomap and Autoencoder, had the worst performance in this study. The results provided recommendations for empirical researchers using machine learning models with high dimensional data under specific conditions.
Author : Ngoc Thanh Nguyen
Publisher : Springer Nature
ISBN 13 : 9819749824
Total Pages : 342 pages
Book Rating : 4.8/5 (197 download)
Download or read book Intelligent Information and Database Systems written by Ngoc Thanh Nguyen and published by Springer Nature. This book was released on with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Paul G. Constantine
Publisher : SIAM
ISBN 13 : 1611973864
Total Pages : 105 pages
Book Rating : 4.6/5 (119 download)
Download or read book Active Subspaces written by Paul G. Constantine and published by SIAM. This book was released on 2015-03-17 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientists and engineers use computer simulations to study relationships between a model's input parameters and its outputs. However, thorough parameter studies are challenging, if not impossible, when the simulation is expensive and the model has several inputs. To enable studies in these instances, the engineer may attempt to reduce the dimension of the model's input parameter space. Active subspaces are an emerging set of dimension reduction tools that identify important directions in the parameter space. This book describes techniques for discovering a model's active subspace and proposes methods for exploiting the reduced dimension to enable otherwise infeasible parameter studies. Readers will find new ideas for dimension reduction, easy-to-implement algorithms, and several examples of active subspaces in action.
Author : Guido Cervone
Publisher : Springer Science & Business Media
ISBN 13 : 1461476690
Total Pages : 175 pages
Book Rating : 4.4/5 (614 download)
Download or read book Data Mining for Geoinformatics written by Guido Cervone and published by Springer Science & Business Media. This book was released on 2013-08-16 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementation of computational algorithms to solve these problems. This unique volume contains a collection of chapters on state-of-the-art data mining techniques applied to geoinformatic problems of high complexity and important societal value. Data Mining for Geoinformatics addresses current concerns and developments relating to spatio-temporal data mining issues in remotely-sensed data, problems in meteorological data such as tornado formation, estimation of radiation from the Fukushima nuclear power plant, simulations of traffic data using OpenStreetMap, real time traffic applications of data stream mining, visual analytics of traffic and weather data and the exploratory visualization of collective, mobile objects such as the flocking behavior of wild chickens. This book is designed for researchers and advanced-level students focused on computer science, earth science and geography as a reference or secondary text book. Practitioners working in the areas of data mining and geoscience will also find this book to be a valuable reference.
Author : Oliver Kramer
Publisher : Springer Science & Business Media
ISBN 13 : 3642386520
Total Pages : 137 pages
Book Rating : 4.6/5 (423 download)
Download or read book Dimensionality Reduction with Unsupervised Nearest Neighbors written by Oliver Kramer and published by Springer Science & Business Media. This book was released on 2013-05-30 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.
Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309287812
Total Pages : 191 pages
Book Rating : 4.3/5 (92 download)
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Author : Graham Elliott
Publisher : Elsevier
ISBN 13 : 0444627405
Total Pages : 667 pages
Book Rating : 4.4/5 (446 download)
Download or read book Handbook of Economic Forecasting written by Graham Elliott and published by Elsevier. This book was released on 2013-08-23 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
