Matrix Methods in Data Mining and Pattern Recognition, Second Edition - 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 Matrix Methods in Data Mining and Pattern Recognition, Second Edition write by Lars Elden. This book was released on 2019-08-30. Matrix Methods in Data Mining and Pattern Recognition, Second Edition available in PDF, EPUB and Kindle. This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application. Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data. The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book. This book is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.
Matrix Methods in Data Mining and Pattern Recognition
Matrix Methods in Data Mining and Pattern Recognition - 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 Matrix Methods in Data Mining and Pattern Recognition write by Lars Elden. This book was released on 2007-07-12. Matrix Methods in Data Mining and Pattern Recognition available in PDF, EPUB and Kindle. Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.
Matrix Methods in Data Mining and Pattern Recognition
Matrix Methods in Data Mining and Pattern Recognition - 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 Matrix Methods in Data Mining and Pattern Recognition write by Lars Elden. This book was released on 2007-01-01. Matrix Methods in Data Mining and Pattern Recognition available in PDF, EPUB and Kindle. This application-oriented book describes how modern matrix methods can be used to solve problems in data mining and pattern recognition, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.
Understanding Complex Datasets
Understanding Complex Datasets - 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 Understanding Complex Datasets write by David Skillicorn. This book was released on 2007-05-17. Understanding Complex Datasets available in PDF, EPUB and Kindle. Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book
Introduction to Matrix Analytic Methods in Stochastic Modeling
Introduction to Matrix Analytic Methods in Stochastic Modeling - 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 Introduction to Matrix Analytic Methods in Stochastic Modeling write by G. Latouche. This book was released on 1999-01-01. Introduction to Matrix Analytic Methods in Stochastic Modeling available in PDF, EPUB and Kindle. Presents the basic mathematical ideas and algorithms of the matrix analytic theory in a readable, up-to-date, and comprehensive manner.
