Dynamic Graph Learning For Dimension Reduction And Data Clustering
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Dynamic Graph Learning For Dimension Reduction And Data Clustering
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Author : Lei Zhu
language : en
Publisher: Synthesis Lectures on Computer Science
Release Date : 2024-09-23
Dynamic Graph Learning For Dimension Reduction And Data Clustering written by Lei Zhu and has been published by Synthesis Lectures on Computer Science this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-23 with categories.
Dynamic Graph Learning For Dimension Reduction And Data Clustering
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Author : Lei Zhu
language : en
Publisher: Springer Nature
Release Date : 2023-09-20
Dynamic Graph Learning For Dimension Reduction And Data Clustering written by Lei Zhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-20 with Computers categories.
This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.
Advances In Data Clustering
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Author : Fadi Dornaika
language : en
Publisher: Springer Nature
Release Date : 2024-12-29
Advances In Data Clustering written by Fadi Dornaika and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-29 with Computers categories.
Clustering, a foundational technique in data analytics, finds diverse applications across scientific, technical, and business domains. Within the theme of “Data Clustering,” this book assumes substantial importance due to its indispensable clustering role in various contexts. As the era of online media facilitates the rapid generation of large datasets, clustering emerges as a pivotal player in data mining and machine learning. At its core, clustering seeks to unveil heterogeneous groups within unlabeled data, representing a crucial unsupervised task in machine learning. The objective is to automatically assign labels to each unlabeled datum with minimal human intervention. Analyzing this data allows for categorization and drawing conclusions applicable across diverse application domains. The challenge with unlabeled data lies in defining a quantifiable goal to guide the model-building process, constituting the central theme of clustering. This book presents concepts and different methodologies of data clustering. For example, deep clustering of images, semi-supervised deep clustering, deep multi-view clustering, etc. This book can be used as a reference for researchers and postgraduate students in related research background.
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Proceedings Of The 21th Acm Sigkdd International Conference On Knowledge Discovery And Data Mining
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Author : Longbing Cao
language : en
Publisher:
Release Date : 2015
Proceedings Of The 21th Acm Sigkdd International Conference On Knowledge Discovery And Data Mining written by Longbing Cao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computer science categories.
Unsupervised Learning Models For Unlabeled Genomic Transcriptomic Proteomic Data
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Author : Jianing Xi
language : en
Publisher: Frontiers Media SA
Release Date : 2022-01-05
Unsupervised Learning Models For Unlabeled Genomic Transcriptomic Proteomic Data written by Jianing Xi and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-05 with Science categories.
Interactive And Dynamic Dashboard
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Author : A. Vadivel
language : en
Publisher: CRC Press
Release Date : 2024-12-31
Interactive And Dynamic Dashboard written by A. Vadivel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.
The text comprehensively discusses the representation of visual data and design principles of interactive and dynamic dashboards. It further covers the theoretical concept of inference and machine learning algorithms for making the concepts clear to the reader. The book illustrates important topics such as data testing a parametric hypothesis, data testing a non-parametric hypothesis, exploratory data analysis, outlier detection and interpretation. This book: Covers various data analysis tools such as KNIME, RapidMiner, Rstudio, Grafana, and Redash Discusses the theoretical concept of inference and machine learning algorithms for designing dynamic dashboards Presents statistical modelling techniques with an emphasis on pattern mining, and pattern relationships Explains the problem of efficient retrieval of similar time series in large databases to enrich the knowledge of the readers to effectively handle various real-time datasets Illustrates dimensionality reduction techniques such as principal component analysis, linear discriminant analysis, singular value decomposition, and piecewise vector quantized approximation It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.
Neural Networks And Statistical Learning
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Author : Ke-Lin Du
language : en
Publisher: Springer Nature
Release Date : 2019-09-12
Neural Networks And Statistical Learning written by Ke-Lin Du and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-12 with Mathematics categories.
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Graph Neural Networks In Action
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Author : Keita Broadwater
language : en
Publisher: Simon and Schuster
Release Date : 2025-04-15
Graph Neural Networks In Action written by Keita Broadwater and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. Ideal for Python programmers, you will dive into graph neural networks perfect for node prediction, link prediction, and graph classification.
Machine Learning Techniques For Multimedia
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Author : Matthieu Cord
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-07
Machine Learning Techniques For Multimedia written by Matthieu Cord and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-07 with Computers categories.
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.