Dimensionality Reduction Feature Extraction And Manifold In Machine Learning
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Elements Of Dimensionality Reduction And Manifold Learning
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Author : Benyamin Ghojogh
language : en
Publisher: Springer Nature
Release Date : 2023-02-02
Elements Of Dimensionality Reduction And Manifold Learning written by Benyamin Ghojogh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-02 with Computers categories.
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.
Modern Multidimensional Scaling
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Author : I. Borg
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-08-04
Modern Multidimensional Scaling written by I. Borg and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-04 with Social Science categories.
The first edition was released in 1996 and has sold close to 2200 copies. Provides an up-to-date comprehensive treatment of MDS, a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. The authors have added three chapters and exercise sets. The text is being moved from SSS to SSPP. The book is suitable for courses in statistics for the social or managerial sciences as well as for advanced courses on MDS. All the mathematics required for more advanced topics is developed systematically in the text.
Dimension Reduction
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Author : Christopher J. C. Burges
language : en
Publisher: Now Publishers Inc
Release Date : 2010
Dimension Reduction written by Christopher J. C. Burges and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
We give a tutorial overview of several foundational methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the data lies. For projective methods, we review projection pursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, canonical correlation analysis (CCA), kernel CCA, Fisher discriminant analysis, oriented PCA, and several techniques for sufficient dimension reduction. For the manifold methods, we review multidimensional scaling (MDS), landmark MDS, Isomap, locally linear embedding, Laplacian eigenmaps, and spectral clustering. Although the review focuses on foundations, we also provide pointers to some more modern techniques. We also describe the correlation dimension as one method for estimating the intrinsic dimension, and we point out that the notion of dimension can be a scale-dependent quantity. The Nystr m method, which links several of the manifold algorithms, is also reviewed. We use a publicly available dataset to illustrate some of the methods. The goal is to provide a self-contained overview of key concepts underlying many of these algorithms, and to give pointers for further reading.
Dimensionality Reduction In Machine Learning
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Author : Jamal Amani Rad
language : en
Publisher: Morgan Kaufmann
Release Date : 2025-02-04
Dimensionality Reduction In Machine Learning written by Jamal Amani Rad and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-04 with Computers categories.
Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. - Provides readers with a comprehensive overview of various dimension reduction algorithms, including linear methods, non-linear methods, and deep learning methods - Covers the implementation aspects of algorithms supported by numerous code examples - Compares different algorithms so the reader can understand which algorithm is suitable for their purpose - Includes algorithm examples that are supported by a Github repository which consists of full notebooks for the programming code
Feature Engineering And Selection
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Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25
Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Introduction To Machine Learning With Python
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Author : Andreas C. Müller
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-09-26
Introduction To Machine Learning With Python written by Andreas C. Müller and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-26 with Computers categories.
Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
Dimensionality Reduction Feature Extraction And Manifold In Machine Learning
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Author : Dr. Neha Sharma
language : en
Publisher: Xoffencerpublication
Release Date : 2023-04-24
Dimensionality Reduction Feature Extraction And Manifold In Machine Learning written by Dr. Neha Sharma and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-24 with Computers categories.
An illustration of a common type of brain-computer interface system (photo courtesy of Gerwin Schalk, Wadsworth Centre, New York) The term "Brain Computer Interfaces," sometimes referred to as BCIs for short, describes a family of technologies that make it possible for people and computers to interact with one another in a direct manner. The word "Brain Computer Interfaces" is shortened as "BCIs" for the shorter version. Brain-computer interfaces, often known as BCIs, offer an alternate means of communication and control to more traditional Human Computer Interfaces (HCIs). These BCIs do not require the user to move their muscles in order to interact with the computer. As a consequence of this, they are particularly useful in applications such as supporting people who have impairments, recovering human cognitive or sensorymotor processes, and improving performance in areas that are pertinent to the tasks at hand. A typical BCI system is comprised of a module for acquiring brain activity, another module for signal preprocessing and feature extraction, a module for classifying mental states or making estimates, and a module for controlling output.. These four modules are referred to together as the BCI stack. Once it was shown that brain impulses might be used to create a mental prosbook, non-invasive brain-computer interfaces, often known as BCIs, have attracted an increasing amount of interest. Brainmachine interfaces (BMIs) are another name for brain-computer interfaces (BCIs). A significant amount of research has been conducted in a wide variety of domains and fields of study. A non-invasive brain-computer interface (BCI) that makes use of electroencephalography (EEG) signals recorded from the scalp may provide people with control over numerous parameters of movement, as Wolpaw and McFarland have demonstrated. It has been demonstrated by that it is possible, with the use of the visual P300 Event Related Potential (ERP), to choose letters that are shown on the screen of a computer.
Recent Advances In Data Mining Of Enterprise Data
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Author : T. Warren Liao
language : en
Publisher: World Scientific
Release Date : 2008-01-15
Recent Advances In Data Mining Of Enterprise Data written by T. Warren Liao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-15 with Business & Economics categories.
The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as OC enterprise dataOCO. The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making. Sample Chapter(s). Foreword (37 KB). Chapter 1: Enterprise Data Mining: A Review and Research Directions (655 KB). Contents: Enterprise Data Mining: A Review and Research Directions (T W Liao); Application and Comparison of Classification Techniques in Controlling Credit Risk (L Yu et al.); Predictive Classification with Imbalanced Enterprise Data (S Daskalaki et al.); Data Mining Applications of Process Platform Formation for High Variety Production (J Jiao & L Zhang); Multivariate Control Charts from a Data Mining Perspective (G C Porzio & G Ragozini); Maintenance Planning Using Enterprise Data Mining (L P Khoo et al.); Mining Images of Cell-Based Assays (P Perner); Support Vector Machines and Applications (T B Trafalis & O O Oladunni); A Survey of Manifold-Based Learning Methods (X Huo et al.); and other papers. Readership: Graduate students in engineering, computer science, and business schools; researchers and practioners of data mining with emphazis of enterprise data mining."
Machine Learning With Python
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Author : Dr. Jyoti Parashar
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2025-05-05
Machine Learning With Python written by Dr. Jyoti Parashar and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-05 with Computers categories.
Machine learning with Python has revolutionized the field of data science, providing a powerful, flexible, and accessible toolkit for creating models that learn from data and make predictions or decisions without being explicitly programmed. Python, with its simplicity and vast ecosystem of libraries, such as Scikit-learn, TensorFlow, Keras, and PyTorch, has become the go-to language for both beginners and experts in the machine learning domain. These libraries offer extensive support for tasks like data preprocessing, model building, evaluation, and optimization. Machine learning algorithms ranging from supervised learning methods such as regression and classification to unsupervised techniques like clustering and dimensionality reduction can be easily implemented and customized in Python to solve real-world problems across various industries, including healthcare, finance, marketing, and autonomous systems. Python's integration with libraries like Pandas and NumPy also enables efficient handling of large datasets, while Matplotlib and Seaborn facilitate comprehensive data visualization for better insights. With the growing popularity of deep learning and neural networks, Python’s role in machine learning continues to expand, driving innovations in areas such as natural language processing (NLP), computer vision, and predictive analytics. Additionally, Python's open-source nature and large community support make it an ideal platform for learning, experimenting, and deploying machine learning models, bridging the gap between research and practical applications. As machine learning continues to evolve, Python remains at the forefront, empowering researchers, developers, and data scientists to create intelligent systems and solve complex problems through data-driven solutions
Nonlinear Dimensionality Reduction
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Author : John A. Lee
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-31
Nonlinear Dimensionality Reduction written by John A. Lee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-31 with Mathematics categories.
Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.