Modern Dimension Reduction - 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 Modern Dimension Reduction write by Philip D. Waggoner. This book was released on 2021-08-05. Modern Dimension Reduction available in PDF, EPUB and Kindle. Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.
Dimension Reduction
Dimension Reduction - 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 Dimension Reduction write by Christopher J. C. Burges. This book was released on 2010. Dimension Reduction available in PDF, EPUB and Kindle. 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.
Special Issue: Modern Dimension Reduction Methods for Big Data Problems in Ecology
Special Issue: Modern Dimension Reduction Methods for Big Data Problems in Ecology - 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 Special Issue: Modern Dimension Reduction Methods for Big Data Problems in Ecology write by Christopher K. Wikle. This book was released on 2013. Special Issue: Modern Dimension Reduction Methods for Big Data Problems in Ecology available in PDF, EPUB and Kindle.
Generalized Principal Component Analysis
Generalized Principal Component Analysis - 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 Generalized Principal Component Analysis write by René Vidal. This book was released on 2016-04-11. Generalized Principal Component Analysis available in PDF, EPUB and Kindle. This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Statistical Methods in Molecular Biology
Statistical Methods in Molecular Biology - 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 Statistical Methods in Molecular Biology write by Heejung Bang. This book was released on 2016-08-23. Statistical Methods in Molecular Biology available in PDF, EPUB and Kindle. This progressive book presents the basic principles of proper statistical analyses. It progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology.
