Robust subspace estimation using low-rank optimization, Oreifej, Omar Shah, Mubarak
Автор: Yun Fu Название: Low-Rank and Sparse Modeling for Visual Analysis ISBN: 3319355678 ISBN-13(EAN): 9783319355672 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.
Автор: Omar Oreifej; Mubarak Shah Название: Robust Subspace Estimation Using Low-Rank Optimization ISBN: 3319352482 ISBN-13(EAN): 9783319352480 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.
Автор: Wang, Zhangyang Название: Deep Learning Through Sparse and Low-Rank Modeling ISBN: 0128136596 ISBN-13(EAN): 9780128136591 Издательство: Elsevier Science Рейтинг: Цена: 13304.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models--those that emphasize problem-specific Interpretability--with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining.
This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks
Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models
Provides tactics on how to build and apply customized deep learning models for various applications
Автор: Yun Fu Название: Low-Rank and Sparse Modeling for Visual Analysis ISBN: 3319119990 ISBN-13(EAN): 9783319119991 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.
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