Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread application in machine learning, neural networks, pattern recognition, image processing, and computer vision. Filling a void in the literature, Manifold Learning Theory and Applications incorporates state-of-the-art techniques in manifold learning...
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningfu...
This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph and graph in vector spaces, and describes their real-world applications.
This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 gra...
Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important human-centered applications.
Human-Centered Social Media Analytics provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. The collected chapters...
Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important ...
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world...
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learnin...
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group...
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image proces...
This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos.
This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recogniti...
Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important human-centered applications.
Human-Centered Social Media Analytics provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. The collected chapters...
Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important ...
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world...
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learnin...
