Download or read book Representation Constrained Canonical Correlation Analysis written by Sudhanshu K. Mishra and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classical canonical correlation analysis is extremely greedy to maximize the squared correlation between two sets of variables. As a result, if one of the variables in the dataset-1 is very highly correlated with another variable in the dataset-2, the canonical correlation will be very high irrespective of the correlation among the rest of the variables in the two datasets. We intend here to propose an alternative measure of association between two sets of variables that will not permit the greed of a select few variables in the datasets to prevail upon the fellow variables so much as to deprive the latter of contributing to their representative variables or canonical variates. Our proposed Representation-Constrained Canonical correlation (RCCCA) Analysis has the Classical Canonical Correlation Analysis (CCCA) at its one end (lambda=0) and the Classical Principal Component Analysis (CPCA) at the other (as lambda tends to be very large). In between it gives us a compromise solution. By a proper choice of lambda, one can avoid hijacking of the representation issue of two datasets by a lone couple of highly correlated variables across those datasets. This advantage of the RCCCA over the CCCA deserves a serious attention by the researchers using statistical tools for data analysis.
Download or read book An Improved Method for Generalized Constrained Canonical Correlation Analysis written by Yoshio Takane and published by . This book was released on 2018 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose an improved method for generalized constrained canonical correlation analysis (GCCANO). In GCCANO, data matrices are first decomposed into several submatrices according to some external information on rows and columns of the data matrices. Decomposed matrices are then subjected to canonical correlation analysis (CANO). However, orthogonal decompositions of data matrices do not necessarily entail the corresponding decompositions of projectors defined by the data matrices. Consequently, no additive partitioning of the total redundancy between two sets of variables was possible in the original GCCANO. In this paper we introduce two orthogonal decompositions of projectors that allow additive partitionings of the total redundancy. Terms in the decompositions have straightforward interpretations. We develop an improved method for GCCANO based on the new decompositions, while preserving the most important features of the original GCCANO. An example is given to illustrate the proposed method.
Download or read book Constrained Principal Component Analysis and Related Techniques written by Yoshio Takane and published by CRC Press. This book was released on 2016-04-19 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? Wha
Download or read book Canonical Correlation Analysis written by Bruce Thompson and published by SAGE. This book was released on 1984-11 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances both in statistical methodology and in computer automation are making canonical correlation analysis available to more and more researchers. In an essentially nonmathematical presentation that provides numerous examples, this volume explains the basic features of this sophisticated technique. Learn more about "The Little Green Book" - QASS Series! Click Here
Download or read book Constrained Canonical Correlation written by Wayne S. DeSarbo and published by . This book was released on 2016 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper explores some of the problems associated with traditional canonical correlation. A response surface methodology is developed to examine the stability of the derived linear functions, where one wishes to investigate how much the coefficients can change and still be in an e-neighborhood of the globally optimum canonical correlation value. In addition, a discrete (or constrained) canonical correlation method is formulated where the derived coefficients of these linear functions are constrained to be in some small set, e.g., {1, 0, -1}, to aid in the interpretation of the results. An example concerning the psychographic responses of Wharton MBA students of the University of Pennsylvania regarding driving preferences and life-style considerations is provided.
Download or read book Multivariate Analysis of Ecological Data using CANOCO 5 written by Petr Šmilauer and published by Cambridge University Press. This book was released on 2014-04-17 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to the theory and practice of multivariate analysis for graduates, researchers and professionals dealing with ecological problems.
Download or read book Canonical Analysis and Factor Comparison written by Mark S. Levine and published by SAGE. This book was released on 1977-04 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Canonical correlational analysis; Factor comparison techniques; References.
Download or read book Applied Multivariate Analysis written by Neil H. Timm and published by Springer Science & Business Media. This book was released on 2007-06-21 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.
Download or read book Multi aspect Learning written by Richi Nayak and published by Springer Nature. This book was released on 2023-08-28 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.
Download or read book Neural Information Processing written by Long Cheng and published by Springer. This book was released on 2018-12-03 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set of LNCS 11301-11307 constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The 5th volume, LNCS 11305, is organized in topical sections on prediction; pattern recognition; and word, text and document processing.
Download or read book System Identification Advances and Case Studies written by and published by Academic Press. This book was released on 1977-02-21 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression. - Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loeve transform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering
Download or read book Nonlinear Canonical Correlation and Some Related Techniques written by Eeke van der Burg and published by . This book was released on 1988 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Prediction and Analysis for Knowledge Representation and Machine Learning written by Avadhesh Kumar and published by CRC Press. This book was released on 2022-01-31 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which includes both basic and advanced concepts.
Download or read book Canonical Correlation Multiple Regression and Simultaneous Systems written by Johny K. Johansson and published by . This book was released on 1974 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Image Analysis written by Josef Bigün and published by Springer Science & Business Media. This book was released on 2003-06-25 with total page 1196 pages. Available in PDF, EPUB and Kindle. Book excerpt: The excellently received call for papers of the 13th Scandinavian Conference on Image Analysis, June 29-July 2 (SCIA 2003) resulted in the selected articles of this proceedings. Additionally the volume also contains invited contributions from - Ivar Austvoll, Stavanger University College (NO), - Lars B? a? ath, Halmstad University (SE), - Ewert Bengtsson, Uppsala University (SE), - Rasmus Larsen, Technical University of Denmark (DK), - Jussi Parkkinen, University of Joensuu (FI), - Pietro Perona, California Institute of Technology (US) which brings the total number of articles to 152. The theme of the papers are dominated by the categories - Feature extraction - Depth and surface - Medical image processing - Shape analysis - Segmentation and spatial grouping - Coding and representation - Motion analysis - Texture analysis - Color analysis - Indexing and categorization which also represent the topical groupings of this book. The particularly strong response to the feature extraction, depth and surface, and medical image processing themes makes us believe that these areas are c- rently expansive, partly because of the rich set of problems which remain to be addressed.
Download or read book Experimental Design and Data Analysis for Biologists written by Gerry P. Quinn and published by Cambridge University Press. This book was released on 2023-08-31 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: A biostatistics textbook for upper undergraduate and graduate students, covering analyses used by biologists and now including R code.
Download or read book The SAGE Handbook of Quantitative Methods in Psychology written by Roger E Millsap and published by SAGE. This book was released on 2009-07-23 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: `I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology′ - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.
