Dynamic Network Representation Based on Latent Factorization of Tensors, Wu
Автор: Panagiotis Symeonidis; Andreas Zioupos Название: Matrix and Tensor Factorization Techniques for Recommender Systems ISBN: 3319413562 ISBN-13(EAN): 9783319413563 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods.
Автор: Wu Название: Robust Latent Feature Learning for Incomplete Big Data ISBN: 9811981396 ISBN-13(EAN): 9789811981395 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
Автор: Naik Ganesh R. Название: Non-Negative Matrix Factorization Techniques: Advances in Theory and Applications ISBN: 3662517000 ISBN-13(EAN): 9783662517000 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book collects new results, concepts and further developments of NMF. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.
Автор: Guanghui Wang; Jonathan Wu Название: Guide to Three Dimensional Structure and Motion Factorization ISBN: 1447125878 ISBN-13(EAN): 9781447125877 Издательство: Springer Рейтинг: Цена: 107130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This practical guide provides a comprehensive overview of Euclidean structure and motion recovery, with a specific focus on factorization-based algorithms. The text discusses the latest research in the field and presents new algorithms developed by the authors.
Автор: Branka Stojanovi?; Oge Marques; Aleksandar Ne?kovi Название: Segmentation and Separation of Overlapped Latent Fingerprints ISBN: 3030233634 ISBN-13(EAN): 9783030233631 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.
By offering a structured overview of the most important approaches currently available, putting them in perspective, and suggesting numerous resources for further exploration, this book gives its readers a clear path for learning new topics and engaging in related research. Written from a technical perspective, and yet using language and terminology accessible to non-experts, it describes the technologies, introduces relevant datasets, highlights the most important research results in each area, and outlines the most challenging open research questions.
This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information.
Автор: Yuan Название: Latent Factor Analysis for High-dimensional and Sparse Matrices ISBN: 9811967024 ISBN-13(EAN): 9789811967023 Издательство: Springer Рейтинг: Цена: 41920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.
Автор: Ganesh R. Naik Название: Non-negative Matrix Factorization Techniques ISBN: 3662483300 ISBN-13(EAN): 9783662483305 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book collects new results, concepts and further developments of NMF. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.
Автор: Steffen Rendle Название: Context-Aware Ranking with Factorization Models ISBN: 3642423973 ISBN-13(EAN): 9783642423970 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Context-aware ranking is an important task in search engine ranking. This book presents a generic method for context-aware ranking as well as its application. It applies this general theory to the three scenarios of item, tag and sequential-set recommendation.
Автор: Song Y. Yan Название: Primality Testing and Integer Factorization in Public-Key Cryptography ISBN: 1441945865 ISBN-13(EAN): 9781441945860 Издательство: Springer Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Intended for advanced level students in computer science and mathematics, this key text, now in a brand new edition, provides a survey of recent progress in primality testing and integer factorization, with implications for factoring based public key cryptography.
Автор: Emmanuel Vincent; Arie Yeredor; Zbyn?k Koldovsk?; Название: Latent Variable Analysis and Signal Separation ISBN: 3319224816 ISBN-13(EAN): 9783319224817 Издательство: Springer Рейтинг: Цена: 59630.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. Five special topics are addressed: tensor-based methods for blind signal separation;
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