IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v269y2015icp956-964.html
   My bibliography  Save this article

Image processing using Newton-based algorithm of nonnegative matrix factorization

Author

Listed:
  • Hu, Li-Ying
  • Guo, Gong-De
  • Ma, Chang-Feng

Abstract

In this paper, we propose a Newton-based algorithm for nonnegative matrix factorization in image processing. We employ the new algorithm to three real-world databases. Extensive numerical results show the feasibility and validity of the proposed algorithm.

Suggested Citation

  • Hu, Li-Ying & Guo, Gong-De & Ma, Chang-Feng, 2015. "Image processing using Newton-based algorithm of nonnegative matrix factorization," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 956-964.
    • Handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:956-964
    DOI: 10.1016/j.amc.2015.08.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300315010899
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2015.08.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. Moghadam & K. Aminian & M. Asghari & M. Parnianpour, 2013. "How well do the muscular synergies extracted via non-negative matrix factorisation explain the variation of torque at shoulder joint?," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(3), pages 291-301.
    2. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    3. Takehiro Sano & Tsuyoshi Migita & Norikazu Takahashi, 2022. "A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence," Journal of Global Optimization, Springer, vol. 84(3), pages 755-781, November.
    4. Duy Khuong Nguyen & Tu Bao Ho, 2017. "Accelerated parallel and distributed algorithm using limited internal memory for nonnegative matrix factorization," Journal of Global Optimization, Springer, vol. 68(2), pages 307-328, June.
    5. FUJIWARA Yoshi & INOUE Hiroyasu & YAMAGUCHI Takayuki & AOYAMA Hideaki & TANAKA Takuma & KIKUCHI Kentaro, 2021. "Money Flow Network Among Firms' Accounts in a Regional Bank of Japan," Discussion papers 21005, Research Institute of Economy, Trade and Industry (RIETI).
    6. Bastian Schaefermeier & Gerd Stumme & Tom Hanika, 2021. "Topic space trajectories," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5759-5795, July.
    7. Yoshi Fujiwara & Rubaiyat Islam, 2021. "Bitcoin's Crypto Flow Network," Papers 2106.11446, arXiv.org, revised Jul 2021.
    8. Yin Liu & Sam Davanloo Tajbakhsh, 2023. "Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient," Journal of Optimization Theory and Applications, Springer, vol. 198(1), pages 239-289, July.
    9. Hiroyasu Abe & Hiroshi Yadohisa, 2019. "Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 825-853, December.
    10. Guowei Yang & Lin Zhang & Minghua Wan, 2022. "Exponential Graph Regularized Non-Negative Low-Rank Factorization for Robust Latent Representation," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
    11. Jingu Kim & Yunlong He & Haesun Park, 2014. "Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework," Journal of Global Optimization, Springer, vol. 58(2), pages 285-319, February.
    12. Hiroyasu Abe & Hiroshi Yadohisa, 2017. "A non-negative matrix factorization model based on the zero-inflated Tweedie distribution," Computational Statistics, Springer, vol. 32(2), pages 475-499, June.
    13. Norikazu Takahashi & Ryota Hibi, 2014. "Global convergence of modified multiplicative updates for nonnegative matrix factorization," Computational Optimization and Applications, Springer, vol. 57(2), pages 417-440, March.
    14. Zhang, Lingsong & Lu, Shu & Marron, J.S., 2015. "Nested nonnegative cone analysis," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 100-110.
    15. Soodabeh Asadi & Janez Povh, 2021. "A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization," Mathematics, MDPI, vol. 9(5), pages 1-22, March.
    16. Yong Gao & Jiajun Liu & Yan Xu & Lan Mu & Yu Liu, 2019. "A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    17. Thiel, Michel & Sauwen, Nicolas & Khamiakova, Tastian & Maes, Tor & Govaerts, Bernadette, 2021. "Comparison of chemometrics strategies for the spectroscopic monitoring of active pharmaceutical ingredients in chemical reactions," LIDAM Discussion Papers ISBA 2021009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Hua Zhou & Kenneth Lange, 2015. "Path following in the exact penalty method of convex programming," Computational Optimization and Applications, Springer, vol. 61(3), pages 609-634, July.
    19. Jason B Castro & Arvind Ramanathan & Chakra S Chennubhotla, 2013. "Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    20. Juan Pineiro-Chousa & Marcos Vizcaíno-González & Samuel Ribeiro-Navarrete, 2019. "Using voting decisions to identify shocks in the financial services industry," Service Business, Springer;Pan-Pacific Business Association, vol. 13(2), pages 419-431, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:956-964. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.