applications principal component analysis (23 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines in detail the correlation, more precisely the weighted correlation and applications involving rankings. A general application is the evaluation of methods to predict rankings. Others involve rankings representing human preferences to infer user preferences; the use of weighted correlation with microarray data and those in the domain of time series. In this book we present new weighted correlation coefficients and new methods of weighted principal component analysis.We also introduce new methods of dimension reduction and clustering for time series data and describe some theoretical results on the weighted correlation coefficients in separate sections.

Taschenbuch. Zustand: Neu. Rankings and Preferences | New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications | Joaquim Pinto Da Costa | Taschenbuch | SpringerBriefs in Statistics | x | Englisch | 2015 | Springer | EAN 9783662483435 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Nonlinear Principal Component Analysis and Its Applications | Yuichi Mori (u. a.) | Taschenbuch | x | Englisch | 2016 | Springer Singapore | EAN 9789811001574 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods. This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods.

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Taschenbuch. Zustand: Neu. Principal Component Analysis and Randomness Test for Big Data Analysis | Practical Applications of RMT-Based Technique | Mieko Tanaka-Yamawaki (u. a.) | Taschenbuch | Evolutionary Economics and Social Complexity Science | vii | Englisch | 2024 | Springer | EAN 9789811939693 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Big Data Analysis by Means of RMT-Oriented Methodologies.- Formulation of the RMT-PCA.- RMT-PCA and Stock Markets.- The RMT-test: New Tool to Measure the Randomness of a Given Sequence.- Application of the RMT-test.- Conclusion.- Appendix I: Introduction to vector, inner product, correlation matrix.- Appendix II: Jacobi's rotation algorithm.- Appendix III: Program for the RMT-test.- Appendix IV: RMT-test applied on TOIPXcore30 index time series in 2014.- Appendix V: RMT-test applied on TOIPX index time series in 2011-2014.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the novel approach of analyzing large-sized rectangular-shaped numerical data (so-called big data). The essence of this approach is to grasp the 'meaning' of the data instantly, without getting into the details of individual data. Unlike conventional approaches of principal component analysis, randomness tests, and visualization methods, the authors' approach has the benefits of universality and simplicity of data analysis, regardless of data types, structures, or specific field of science. First, mathematical preparation is described. The RMT-PCA and the RMT-test utilize the cross-correlation matrix of time series,C=XXT, whereXrepresents a rectangular matrix ofNrows andLcolumns andXTrepresents the transverse matrix ofX. BecauseCis symmetric, namely,C=CT, it can be converted to a diagonal matrix of eigenvalues by a similarity transformationSCS-1=SCSTusing an orthogonal matrixS. WhenNis significantly large, the histogram of the eigenvalue distribution can be compared to the theoretical formula derived in the context of the random matrix theory (RMT, in abbreviation). Then the RMT-PCA applied to high-frequency stock prices in Japanese and American markets is dealt with. This approach proves its effectiveness in extracting 'trendy' business sectors of the financial market over the prescribed time scale. In this case,Xconsists ofNstock- prices of lengthL, and the correlation matrixCis anNbyNsquare matrix, whose element at thei-th row andj-th column is the inner product of the price time series of the lengthLof thei-th stock and thej-th stock of the equal lengthL. Next, the RMT-test is applied to measure randomness of various random number generators, including algorithmically generated random numbers and physically generated random numbers. The book concludes by demonstrating two applications of the RMT-test: (1) a comparison of hash functions, and (2) stock prediction by means of randomness, including a new index of off-randomness related to market decline.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the novel approach of analyzing large-sized rectangular-shaped numerical data (so-called big data). The essence of this approach is to grasp the 'meaning' of the data instantly, without getting into the details of individual data. Unlike conventional approaches of principal component analysis, randomness tests, and visualization methods, the authors' approach has the benefits of universality and simplicity of data analysis, regardless of data types, structures, or specific field of science. First, mathematical preparation is described. The RMT-PCA and the RMT-test utilize the cross-correlation matrix of time series,C=XXT, whereXrepresents a rectangular matrix ofNrows andLcolumns andXTrepresents the transverse matrix ofX. BecauseCis symmetric, namely,C=CT, it can be converted to a diagonal matrix of eigenvalues by a similarity transformationSCS-1=SCSTusing an orthogonal matrixS. WhenNis significantly large, the histogram of the eigenvalue distribution can be compared to the theoretical formula derived in the context of the random matrix theory (RMT, in abbreviation). Then the RMT-PCA applied to high-frequency stock prices in Japanese and American markets is dealt with. This approach proves its effectiveness in extracting 'trendy' business sectors of the financial market over the prescribed time scale. In this case,Xconsists ofNstock- prices of lengthL, and the correlation matrixCis anNbyNsquare matrix, whose element at thei-th row andj-th column is the inner product of the price time series of the lengthLof thei-th stock and thej-th stock of the equal lengthL. Next, the RMT-test is applied to measure randomness of various random number generators, including algorithmically generated random numbers and physically generated random numbers. The book concludes by demonstrating two applications of the RMT-test: (1) a comparison of hash functions, and (2) stock prediction by means of randomness, including a new index of off-randomness related to market decline.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book emphasizes on the diverse applications of principal component analysis. The aim of this book is to enhance knowledge of scientists, engineers and researchers regarding the advantages of this technique in data analysis. It includes the uses of PCA in distinct fields like agriculture, architecture, taxonomy, pharmacy, ecology, biology, health and finance.