Robust Graph Regularized Non-negative Matrix Factorization for Image Clustering
Non-negative matrix factorization and its variants have been utilized for computer vision and machine learning, however, they fail to achieve robust factorization when the dataset is corrupted by outliers and noise. In this paper, we propose a roust graph
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Min Han Sitian Qin Nian Zhang (Eds.)
Advances in Neural Networks – ISNN 2020 17th International Symposium on Neural Networks, ISNN 2020 Cairo, Egypt, December 4–6, 2020 Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this subseries at http://www.springer.com/series/7407
Min Han Sitian Qin Nian Zhang (Eds.) •
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Advances in Neural Networks – ISNN 2020 17th International Symposium on Neural Networks, ISNN 2020 Cairo, Egypt, December 4–6, 2020 Proceedings
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Editors Min Han Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian, China
Sitian Qin School of Science Harbin Institute of Technology Weihai, China
Nian Zhang School of Engineering and Applied Sciences University of the District of Columbia Washington, DC, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-64220-4 ISBN 978-3-030-64221-1 (eBook) https://doi.org/10.1007/978-3-030-64221-1 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This volume of Lecture Notes in Computer Science (LNCS) constitutes the proceedings of the 17th International Symposium on Neural Networks (ISNN 2020), held
