IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0183933.html
   My bibliography  Save this article

Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

Author

Listed:
  • Y-h Taguchi

Abstract

In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as Nm, which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies.

Suggested Citation

  • Y-h Taguchi, 2017. "Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-36, August.
    • Handle: RePEc:plo:pone00:0183933
    DOI: 10.1371/journal.pone.0183933
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183933
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183933&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0183933?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
    ---><---

    References listed on IDEAS

    as
    1. Hyun-Soo Cho & Karen Mason & Kasra X. Ramyar & Ann Marie Stanley & Sandra B. Gabelli & Dan W. Denney & Daniel J. Leahy, 2003. "Structure of the extracellular region of HER2 alone and in complex with the Herceptin Fab," Nature, Nature, vol. 421(6924), pages 756-760, February.
    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. Claudia L. Driscoll & Anthony H. Keeble & Mark R. Howarth, 2024. "SpyMask enables combinatorial assembly of bispecific binders," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Harish Shankaran & Yi Zhang & Yunbing Tan & Haluk Resat, 2013. "Model-Based Analysis of HER Activation in Cells Co-Expressing EGFR, HER2 and HER3," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-15, August.
    3. Carmen Peess & Leopold von Proff & Sabine Goller & Karl Andersson & Michael Gerg & Magnus Malmqvist & Birgit Bossenmaier & Michael Schräml, 2015. "Deciphering the Stepwise Binding Mode of HRG1β to HER3 by Surface Plasmon Resonance and Interaction Map," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-13, February.
    4. Mary S. Morrison & Tina Wang & Aditya Raguram & Colin Hemez & David R. Liu, 2021. "Disulfide-compatible phage-assisted continuous evolution in the periplasmic space," Nature Communications, Nature, vol. 12(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0183933. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.