{"product_id":"9783032322630","title":"High-Dimensional Imaging Data Analysis From Classical PCA to Tensor Robust PCA with AI\/ML, Vol. 1","description":"\u003ch3\u003eICSA Book Series in Statistics\u003c\/h3\u003e\u003ch1\u003eHigh-Dimensional Imaging Data Analysis\u003c\/h1\u003e\u003ch2\u003eFrom Classical PCA to Tensor Robust PCA with AI\/ML, Vol. 1\u003c\/h2\u003e\u003ch3\u003eHabte Tadesse Likassa | Ding-Geng Chen\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMedical \/ Biostatistics\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\u003cp\u003eThis book provides a comprehensive, modern treatment of Principal Component Analysis (PCA) and its robust extensions for high-dimensional data analysis, with a particular emphasis on image data, machine learning (ML) and artificial intelligence (AI) applications, and optimization-based methods. Classical PCA remains a foundational tool for dimensionality reduction, outlier detection, feature extraction, and data image visualization and reconstruction; however, its sensitivity to outliers, noise, missing data, and gross corruption severely limits its applicability in real-world problems. This book addresses these limitations by developing a unified and rigorous framework for Robust PCA and its advanced variants.\u003cbr\u003eThe scope of the book spans from classical PCA to state-of-the-art robust low-rank modelling techniques, including Robust PCA with weighted nuclear norm, norm-based robustness, truncated weighted nuclear norm RPCA, and tensor robust PCA for high-dimensional imaging data.. Particular attention is given to ADMM and related optimization strategies that enable scalable solutions for high-dimensional problems.\u003cbr\u003eThe core arguments of the book are threefold. First, robustness is essential not optional in modern data analysis, especially for high-dimensional image and tensor data contaminated by outliers, occlusions, and structured noise. Second, carefully incorporated regularization, such as weighted and truncated nuclear norms and structured sparsity via the  norm, substantially improves recovery accuracy and interpretability compared with standard RPCA. Third, many seemingly distinct methods in machine learning and AI, and low-rank modelling can be understood within a unified optimization framework, enabling principled algorithm design and theoretical insight.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eHabte Tadesse Likassa is a Senior Research Data Scientist in the College of Health Solutions at Arizona State University. His research focuses on the development of low rank sparse decomposition methods for an imaging data, robust methods, machine learning algorithms, AI techniques and low-rank and sparse representations, with applications to image analysis, biomedical data, and complex structured data. His work emphasizes the development and implementation of robust PCA, weighted and truncated nuclear norm methods, tensor-based models, and optimization algorithms such as ADMM for large-scale data analysis. Dr. Likassa has contributed to methodological research at the intersection of robust statistics, regularization techniques, and computational algorithms, with particular interest in improving stability, interpretability, and performance in the presence of noise and outliers. He has authored and co-authored peer-reviewed publications in statistics, data science, and applied methodology journals, and has experience collaborating on interdisciplinary research projects in health and biomedical sciences.\u003c\/p\u003e\r\n\u003cp\u003eDing-Geng Chen is a Professor of Biostatistics and Executive Director in the College of Health Solutions at Arizona State University and an internationally recognized researcher in statistical methodology, biostatistics, and data science. His research interests include robust statistical methods, longitudinal and multivariate analysis, high-dimensional data modelling, and computational statistics, with broad applications in health sciences and interdisciplinary research. Dr. Chen has an extensive publication record, having authored and co-authored numerous peer-reviewed journal articles, books, and book chapters in leading statistical and applied journals. He has also served as an editor and editorial board member for several professional journals and has played a significant role in advancing methodological research and graduate education in statistics and biostatistics.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e10 September 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032322630\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":50547287490700,"sku":"9783032322630","price":143.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032322630.jpg?v=1781059410","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032322630","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}