Multi-aspect Learning : Methods and Applications

Khanh Luong

ISBN 10: 3031335627 / ISBN 13: 9783031335624

Published by Springer International Publishing, Springer International Publishing, 2024

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.

Seller Inventory # 9783031335624

Bibliographic Details

• Title: Multi-aspect Learning : Methods and Applications
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2024
• Language: English
• ISBN 10: 3031335627
• ISBN 13: 9783031335624
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 11D millimeters 235 millimeters width by 155 millimeters height by 11 millimeters depth
• Weight: 335 grams

About this title

Synopsis

This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.

About the Author

Richi Nayak is a Professor at the School of Computer Science and Leader of the Complex Data Analysis Program at the Centre of Data Science at Queensland University of Technology, Brisbane Australia. She has gained international recognition for her expertise in machine learning, data mining and text mining. Her research has resulted in significant advancements in clustering, deep neural networks, social media mining, recommender systems, multi-view learning and tensor/matrix factorization. She is highly passionate about addressing societal issues by applying her machine learning and AI innovation and fundamental research. She regularly consults with private, public and government agencies on various machine learning projects, many of which have been commercialised. Her research contributions have led to novel solutions for problems in Digital Marketing, K-12 Education, Digital Agriculture and Digital Humanities. She has authored more than 250 high-quality refereed publications that have been cited over 4000 citations, with an h-index of 33.  She has been recognized for her research leadership with several best paper awards and nominations at international conferences, QUT Postgraduate Research Supervision awards, and the 2016 Women in Technology (WiT) Infotech Outstanding Achievement Award in Australia. She also serves as a Steering committee member of the Australasian Data Mining and Machine Learning Conference and as the editorial chief of the International Journal of Data Mining and Digital Humanities. She holds a PhD in Computer Science from the Queensland University of Technology and a Masters in Engineering from the Indian Institute of Technology Roorkee, India.

 

Khanh Luong obtained her PhD in Computer Science specializing in Data Science from Queensland University of Technology (QUT) in 2019. Afterwards, she worked as a Postdoctoral Researcher in Data Science at the QUT Centre for Data Science, where her research focused on addressing the challenges of dealing with multiple aspect data. Her research has made significant contributions to the fields of machine learning and data mining by developing innovative methods ready to be deployed on real-world datasets, ranging from text, image, sound, video, and bioinformatics data. Her methods apply to diverse problems, such as clustering, classification, anomaly detection, community discovery, and collaborative filtering, with a novel multi-aspect outlook. She has an impressive track record as an active member of the Organizing Committee of the Australasian Data Mining Conference for several years. Additionally, she has established herself as a highly regarded reviewer for several top-tier journals, including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Knowledge Discovery from Data (TKDD), IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), IEEE Transactions on Audio, Speech and Language Processing (TASLP), and Information Sciences. Recently joining Charles Sturt University as a research fellow, she is currently working on Cyber Security projects and collaborating with Data61 to develop practical approaches for detecting and reacting to attacks using various data sources.