Principal Manifolds for Data Visualization and Dimension Reduction PDF Download
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Author: Alexander N. Gorban
Publisher: Springer Science & Business Media
ISBN: 3540737499
Category : Computers
Languages : en
Pages : 361
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Book Description
The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
Author: Alexander N. Gorban
Publisher: Springer Science & Business Media
ISBN: 3540737499
Category : Computers
Languages : en
Pages : 361
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Book Description
The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
Author: Alexander N. Gorban
Publisher: Springer Science & Business Media
ISBN: 3540737502
Category : Technology & Engineering
Languages : en
Pages : 340
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Book Description
The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
Author: John A. Lee
Publisher: Springer
ISBN: 9781441922885
Category : Mathematics
Languages : en
Pages : 0
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Book Description
This book describes established and advanced methods for reducing the dimensionality of numerical databases. Each description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. The text provides a lucid summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all described from a unifying point of view, which helps to highlight their respective strengths and shortcomings. The presentation will appeal to statisticians, computer scientists and data analysts, and other practitioners having a basic background in statistics or computational learning.
Author: Olivas, Emilio Soria
Publisher: IGI Global
ISBN: 1605667676
Category : Computers
Languages : en
Pages : 852
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Book Description
"This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.
Author: B.K. Tripathy
Publisher: CRC Press
ISBN: 1000438317
Category : Business & Economics
Languages : en
Pages : 174
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Book Description
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. FEATURES Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples, and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
Author: Emilio Corchado
Publisher: Springer
ISBN: 3540876561
Category : Computers
Languages : en
Pages : 767
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Book Description
The Third International Workshop on Hybrid Artificial Intelligence Systems (HAIS 2008) presented the most recent developments in the dynamically expanding realm of symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques. Hybrid intelligent systems have become incre- ingly popular given their capabilities to handle a broad spectrum of real-world c- plex problems which come with inherent imprecision, uncertainty and vagueness, high-dimensionality, and non stationarity. These systems provide us with the oppor- nity to exploit existing domain knowledge as well as raw data to come up with prom- ing solutions in an effective manner. Being truly multidisciplinary, the series of HAIS workshops offers a unique research forum to present and discuss the latest theoretical advances and real-world applications in this exciting research field. This volume of Lecture Notes on Artificial Intelligence (LNAI) includes accepted papers presented at HAIS 2008 held in University of Burgos, Burgos, Spain, Sept- ber 2008 The global purpose of HAIS conferences has been to form a broad and interdis- plinary forum for hybrid artificial intelligence systems and associated learning pa- digms, which are playing increasingly important roles in a large number of application areas. Since its first edition in Brazil in 2006, HAIS has become an important forum for researchers working on fundamental and theoretical aspects of hybrid artificial intel- gence systems based on the use of agents and multiagent systems, bioinformatics and bio-inspired models, fuzzy systems, artificial vision, artificial neural networks, opti- zation models and alike.
Author: Boris Kovalerchuk
Publisher: Springer Nature
ISBN: 3030931196
Category : Technology & Engineering
Languages : en
Pages : 671
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Book Description
This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.
Author: Masaaki Kurosu
Publisher: Springer Nature
ISBN: 3030784622
Category : Computers
Languages : en
Pages : 657
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Book Description
The three-volume set LNCS 12762, 12763, and 12764 constitutes the refereed proceedings of the Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021, which took place virtually in July 2021. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The 139 papers included in this HCI 2021 proceedings were organized in topical sections as follows: Part I, Theory, Methods and Tools: HCI theory, education and practice; UX evaluation methods, techniques and tools; emotional and persuasive design; and emotions and cognition in HCI Part II, Interaction Techniques and Novel Applications: Novel interaction techniques; human-robot interaction; digital wellbeing; and HCI in surgery Part III, Design and User Experience Case Studies: Design case studies; user experience and technology acceptance studies; and HCI, social distancing, information, communication and work
Author: Benyamin Ghojogh
Publisher: Springer Nature
ISBN: 3031106024
Category : Computers
Languages : en
Pages : 617
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Book Description
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: Andreas Fink
Publisher: Springer Science & Business Media
ISBN: 364201044X
Category : Computers
Languages : en
Pages : 695
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Book Description
Data Analysis, Data Handling and Business Intelligence are research areas at the intersection of computer science, artificial intelligence, mathematics, and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as in marketing, finance, economics, engineering, linguistics, archaeology, musicology, medical science, and biology. This volume contains the revised versions of selected papers presented during the 32nd Annual Conference of the German Classification Society (Gesellschaft für Klassifikation, GfKl). The conference, which was organized in cooperation with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), was hosted by Helmut-Schmidt-University, Hamburg, Germany, in July 2008.
