<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="5.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.crossref.org/schema/5.4.0" xsi:schemaLocation="http://www.crossref.org/schema/5.4.0 https://www.crossref.org/schemas/crossref5.4.0.xsd" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:fr="http://www.crossref.org/fundref.xsd" xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" xmlns:rel="http://www.crossref.org/relations.xsd" xmlns:mml="http://www.w3.org/1998/Math/MathML">
  <head>
    <doi_batch_id>NONE</doi_batch_id>
    <timestamp>20260512111535293</timestamp>
    <depositor>
      <depositor_name>wseas/wseas</depositor_name>
      <email_address>content-registration-form+ja@crossref.org</email_address>
    </depositor>
    <registrant>content-registration-form</registrant>
  </head>
  <body>
    <journal>
      <journal_metadata>
        <full_title>WSEAS TRANSACTIONS ON MATHEMATICS</full_title>
        <issn media_type="print">1109-2769</issn>
        <issn media_type="electronic">2224-2880</issn>
      </journal_metadata>
      <journal_article>
        <titles>
          <title>Coordinate-Independent Formulation of Matrix Division</title>
        </titles>
        <contributors>
          <person_name sequence="first" contributor_role="author">
            <given_name>Hasan</given_name>
            <surname>Keleş</surname>
            <affiliations>
              <institution>
                <institution_name>Department of Mathematics Karadeniz Technical University Campus of Kanuni, Ortahisar, 61080 Trabzon TÜRKÍYE</institution_name>
              </institution>
            </affiliations>
          </person_name>
        </contributors>
        <publication_date media_type="print">
          <month>05</month>
          <day>12</day>
          <year>2026</year>
        </publication_date>
        <publication_date media_type="online">
          <month>05</month>
          <day>12</day>
          <year>2026</year>
        </publication_date>
        <pages>
          <first_page>86</first_page>
        </pages>
        <publisher_item>
          <item_number item_number_type="article_number">10</item_number>
        </publisher_item>
        <ai:program name="AccessIndicators">
          <ai:license_ref>https://creativecommons.org/licenses/by/4.0/deed.en_US</ai:license_ref>
        </ai:program>
        <doi_data>
          <doi>10.37394/23206.2026.25.10</doi>
          <resource>https://wseas.com/journals/mathematics/2026/a205106-005(2026).pdf</resource>
        </doi_data>
        <citation_list>
          <citation key="ref0">
            <unstructured_citation>H. Keleş, “Poloid and Monoid,” The Aligarh Bulletin of Mathematics, vol. 42, no. 1, pp. 15–21, 2023. Available: https://www.tabm aths.com/issues/Tabmath-Vol42-Numbe r-1-2023/5-Paper-2.pdf. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref1">
            <unstructured_citation>H. Keleş, “Different Approaches on the Matrix Division and Generalization of Cramer’s Rule,” Journal of Scientific and Engineering Research, vol. 4, no. 3, pp. 105–108, 2017. Available: ht tps://jsaer.com/download/vol-4-iss -3-2017/JSAER2017-04-03-105-108.pdf. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref2">
            <unstructured_citation>H. Keleş, “On Matrix Division and Rational Matrices,” Journal of Scientific and Engineering Research, vol. 5, no. 8, pp. 279–286, 2018. Available: https://jsaer.com/download/vol-5-iss -8-2018/JSAER2018-05-08-279-286.pdf. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref3">
            <unstructured_citation>H. Keleş, “On Some Results on Row Co-Division in Regular Square Matrices,” in 4th International Palandoken Scientific Studies Congress, Erzurum, Turkey, 2022, pp. 243–249. ISBN: 978-625-8324-45-1. Available: https://www.isarconference.org/_file s/ugd/6dc816_d2b90d3278be434c8eba648 a1c1e0c34.pdf. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref4">
            <unstructured_citation>H. Keleş, “On the Involutive Matrices of the kth Degree,” Iraqi Journal for Computer Science and Mathematics, vol. 4, no. 1, pp. 10–14, 2023. DOI: 10.52866/ijcsm.2023.01.01.002.</unstructured_citation>
          </citation>
          <citation key="ref5">
            <unstructured_citation>J. Pickard, C. Chen, C. Stansbury, A. Surana, A. Bloch, and I. Rajapakse, “Kronecker Product of Tensors and Hypergraphs: Structure and Dynamics,” SIAM Journal on Matrix Analysis and Applications, vol. 45, no. 3, pp. 1621–1642, 2024. DOI: 10.1137/23M1592547.</unstructured_citation>
          </citation>
          <citation key="ref6">
            <unstructured_citation>N. J. Higham, “Cayley, Sylvester, and Early Matrix Theory,” Linear Algebra and its Applications, vol. 428, pp. 39–43, 2008. DOI: 10.1016/j.laa.2007.10.004.</unstructured_citation>
          </citation>
          <citation key="ref7">
            <unstructured_citation>S. Athloen and R. McLaughlin, “Gauss-Jordan reduction: A brief history,” American Mathematical Monthly, vol. 94, no. 2, pp. 130–142, 1987. DOI: 10.1080/00029890.1987.12000605.</unstructured_citation>
          </citation>
          <citation key="ref8">
            <unstructured_citation>S. Zakrzewski, B. Stasiak, and A. Wojciechowski, “Supervised factor selection in tensor decomposition of EEG signal,” Computer Methods and Programs in Biomedicine, vol. 269, p. 108866, Sep. 2025. DOI: 10.1016/j.cmpb.2025.108866.</unstructured_citation>
          </citation>
          <citation key="ref9">
            <unstructured_citation>I. V. Oseledets, “Tensor-Train Decomposition,” SIAM Journal on Scientific Computing, vol. 33, no. 5, pp. 2295–2317, 2011. DOI: 10.1137/090752286.</unstructured_citation>
          </citation>
          <citation key="ref10">
            <unstructured_citation>T. G. Kolda and B. W. Bader, “Tensor Decompositions and Applications,” SIAM Review, vol. 51, no. 3, pp. 455–500, 2009. DOI: 10.1137/07070111X.</unstructured_citation>
          </citation>
          <citation key="ref11">
            <unstructured_citation>T. G. Kolda and J. R. Mayo, “Shifted Power Method for Computing Tensor Eigenpairs,” SIAM Journal on Matrix Analysis and Applications, vol. 32, no. 4, pp. 1095–1124, 2011. DOI: 10.1137/100801482.</unstructured_citation>
          </citation>
          <citation key="ref12">
            <unstructured_citation>N. D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E. E. Papalexakis, and C. Faloutsos, “Tensor Decomposition for Signal Processing and Machine Learning,” IEEE Transactions on Signal Processing, vol. 65, no. 13, pp. 3551–3582, 2017. DOI: 10.1109/TSP.2017.2690524.</unstructured_citation>
          </citation>
          <citation key="ref13">
            <unstructured_citation>L. De Lathauwer, B. De Moor, and J. Vandewalle, “A Multilinear Singular Value Decomposition,” SIAM Journal on Matrix Analysis and Applications, vol. 21, no. 4, pp. 1253–1271, 2000. DOI: 10.1137/S0895479896305696.</unstructured_citation>
          </citation>
          <citation key="ref14">
            <unstructured_citation>L. De Lathauwer, B. De Moor, and J. Vandewalle, “On the Best Rank-1 and Rank-(R1, R2, . . . , RN ) Approximation of Higher-Order Tensors,” SIAM Journal on Matrix Analysis and Applications, vol. 21, no. 4, pp. 1324–1342, 2000. DOI: 10.1137/S0895479898346995.</unstructured_citation>
          </citation>
          <citation key="ref15">
            <unstructured_citation>J. D. Carroll and J. J. Chang, “Analysis of individual differences in multidimensional scaling via an n-way generalization of ’Eckart-Young’ decomposition,” Psychometrika, vol. 35, no. 3, pp. 283–319, 1970. DOI: 10.1007/BF02310791.</unstructured_citation>
          </citation>
          <citation key="ref16">
            <unstructured_citation>R. A. Harshman, “Foundations of the PARAFAC procedure: Models and conditions for an ’explanatory’ multimodal factor analysis,” UCLA Working Papers in Phonetics, vol. 16, pp. 1–84, 1970. Available: https://www.psychology.uwo.ca/facult y/harshman/wpppfac0.pdf. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref17">
            <unstructured_citation>W. Hackbusch and S. Kühn, “A New Scheme for the Tensor Representation,” Journal of Fourier Analysis and Applications, vol. 15, no. 5, pp. 706–722, 2009. DOI: 10.1007/s00041-009-9094-9.</unstructured_citation>
          </citation>
          <citation key="ref18">
            <unstructured_citation>S. Friedland, “On the generic and typical rank of 3-tensors,” Linear Algebra and its Applications, vol. 436, no. 3, pp. 478–497, 2012. DOI: 10.1016/j.laa.2011.05.008.</unstructured_citation>
          </citation>
          <citation key="ref19">
            <unstructured_citation>L. Grasedyck, “Hierarchical Singular Value Decomposition of Tensors,” SIAM Journal on Matrix Analysis and Applications, vol. 31, no. 4, pp. 2029–2054, 2010. DOI: 10.1137/090764189.</unstructured_citation>
          </citation>
          <citation key="ref20">
            <unstructured_citation>A. Cichocki, D. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, and H. A. Phan, “Tensor Decompositions for Signal Processing Applications: From Two-way to Multiway Component Analysis,” IEEE Signal Processing Magazine, vol. 32, no. 2, pp. 145–163, 2015. DOI: 10.1109/MSP.2013.2297439.</unstructured_citation>
          </citation>
          <citation key="ref21">
            <unstructured_citation>M. E. Kilmer and C. D. Martin, “Factorization strategies for third-order tensors,” Linear Algebra and its Applications, vol. 435, no. 3, pp. 641–658, 2011. DOI: 10.1016/j.laa.2010.09.020.</unstructured_citation>
          </citation>
          <citation key="ref22">
            <unstructured_citation>M. E. Kilmer, K. Braman, N. Hao, and R. C. Hoover, “Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging,” SIAM Journal on Imaging Sciences, vol. 34, no. 1, pp. 148–172, 2013. DOI: 10.1137/110837711.</unstructured_citation>
          </citation>
          <citation key="ref23">
            <unstructured_citation>B. W. Bader and T. G. Kolda, “Algorithm 862: MATLAB Tensor Classes for Fast Algorithm Prototyping,” ACM Transactions on Mathematical Software, vol. 32, no. 4, pp. 635–653, 2006. DOI: 10.1145/1186785.1186794.</unstructured_citation>
          </citation>
          <citation key="ref24">
            <unstructured_citation>L. Qi, “Eigenvalues of a real supersymmetric tensor,” Journal of Symbolic Computation, vol. 40, no. 6, pp. 1302–1324, 2005. DOI: 10.1016/j.jsc.2005.05.007.</unstructured_citation>
          </citation>
          <citation key="ref25">
            <unstructured_citation>A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky, “Tensor Decompositions for Learning Latent Variable Models, arxic preprint, 2014. Available: https://arxiv.org/abs/ 1210.7559. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref26">
            <unstructured_citation>E. Acar and B. Yener, “Unsupervised Multi-way Data Analysis: A Literature Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 1, pp. 6–20, 2009. DOI: 10.1109/TKDE.2008.112.</unstructured_citation>
          </citation>
          <citation key="ref27">
            <unstructured_citation>N. Hao, M. E. Kilmer, K. Braman, and R. C. Hoover, “Facial Recognition Using Tensor-Tensor Decompositions,” SIAM Journal on Imaging Sciences, vol. 6, no. 1, pp. 437–463, 2013. DOI: 10.1137/110842570.</unstructured_citation>
          </citation>
          <citation key="ref28">
            <unstructured_citation>M. Mørup, “Applications of tensor (multiway array) factorizations and decompositions in data mining,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 24–40, 2011. DOI: 10.1002/widm.1.</unstructured_citation>
          </citation>
          <citation key="ref29">
            <unstructured_citation>R. Bro, “PARAFAC. Tutorial and applications,” Chemometrics and Intelligent Laboratory Systems, vol. 38, no. 2, pp. 149–171, 1997. DOI: 10.1016/S0169-7439(97)00032-4.</unstructured_citation>
          </citation>
          <citation key="ref30">
            <unstructured_citation>K. Charalampous and A. Gasteratos, “A tensor-based deep learning framework,” Image and Vision Computing, vol. 32, no. 11, pp. 916–929, 2014. DOI: 10.1016/j.imavis.2014.08.003.</unstructured_citation>
          </citation>
          <citation key="ref31">
            <unstructured_citation>T. G. Kolda, “Orthogonal Tensor Decompositions,” SIAM Journal on Matrix Analysis and Applications, vol. 23, no. 1, pp. 243–255, 2001. DOI: 10.1137/S0895479800368354.</unstructured_citation>
          </citation>
          <citation key="ref32">
            <unstructured_citation>T. Zhang and G. H. Golub, “Rank-one approximations to high order tensors,” SIAM Journal on Matrix Analysis and Applications, vol. 23, no. 2, pp. 534–550, 2001. DOI: 10.1137/S0895479899352045.</unstructured_citation>
          </citation>
          <citation key="ref33">
            <unstructured_citation>H. Kong, X. Xie, and Z. Lin, “t-Schatten-p Norm for Low-Rank Tensor Recovery,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, pp. 1405–1419, 2018. DOI: 10.1109/JSTSP.2018.2879185.</unstructured_citation>
          </citation>
          <citation key="ref34">
            <unstructured_citation>S. Rabanser, O. Shchur, and S. Günnemann, “Introduction to Tensor Decompositions and Their Applications in Machine Learning,” 2017. arXiv:1711.10781. DOI: 10.48550/arXiv.1711.10781.</unstructured_citation>
          </citation>
          <citation key="ref35">
            <unstructured_citation>F. Le Gall, “Powers of Tensors and Fast Matrix Multiplication,” in Proceedings of the 39th International Symposium on Symbolic and Algebraic Computation (ISSAC ’14), Kobe, Japan, 2014, pp. 296–303. DOI: 10.1145/2608628.2608664.</unstructured_citation>
          </citation>
          <citation key="ref36">
            <unstructured_citation>J. M. Landsberg, Tensors: Geometry and Applications (Graduate Studies in Mathematics, vol. 128). Providence, RI, USA: American Mathematical Society, 2012. DOI: 10.1090/gsm/128.</unstructured_citation>
          </citation>
          <citation key="ref37">
            <unstructured_citation>P. Comon and B. Mourrain, “Decomposition of quantics in sums of powers of linear forms,” Signal Processing, vol. 53, no. 2-3, pp. 93–107, 1996. DOI: 10.1016/0165-1684(96)00079-5.</unstructured_citation>
          </citation>
          <citation key="ref38">
            <unstructured_citation>D. Tikk, P. Baranyi, R. J. Patton, I. Rudas, and J. K. Tar, “Design methodology of tensor product based control models via HOSVD and LMIs,” in 2002 IEEE International Conference on Industrial Technology (ICIT ’02), vol. 2, pp. 1290–1295, 2002. DOI: 10.1109/ICIT.2002.1189363.</unstructured_citation>
          </citation>
          <citation key="ref39">
            <unstructured_citation>I. V. Oseledets and E. E. Tyrtyshnikov, “TT-cross approximation of multidimensional arrays,” Linear Algebra and its Applications, vol. 432, no. 1, pp. 70–88, 2010. DOI: 10.1016/j.laa.2009.07.024.</unstructured_citation>
          </citation>
          <citation key="ref40">
            <unstructured_citation>J. B. Kruskal, “Three-way arrays: rank and uniqueness of trilinear decompositions,” Linear Algebra and its Applications, vol. 18, no. 2, pp. 95–138, 1977. DOI: 10.1016/0024-3795(77)90069-6.</unstructured_citation>
          </citation>
          <citation key="ref41">
            <unstructured_citation>A. Cichocki, A.-H. Phan, Q. Zhao, N. Lee, I. V. Oseledets, M. Sugiyama, and D. Mandic, “Tensor Networks for Dimensionality Reduction and Large-scale Optimization,” Foundations and Trends in Machine Learning, vol. 9, no. 4-5, pp. 249–429, 2016. DOI: 10.1561/2200000059.</unstructured_citation>
          </citation>
          <citation key="ref42">
            <unstructured_citation>F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Journal of Mathematics and Physics, vol. 6, no. 1-4, pp. 164–189, 1927. DOI: 10.1002/sapm192761164.</unstructured_citation>
          </citation>
          <citation key="ref43">
            <unstructured_citation>J. Chen and W. Huang, “Rank-one approximation of a higher-order tensor by a Riemannian trust-region method,” Computational Optimization and Applications, vol. 90, no. 2, pp. 515–556, March 2025. DOI: 10.1007/s10589-024-00634-z.</unstructured_citation>
          </citation>
          <citation key="ref44">
            <unstructured_citation>L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika, vol. 31, no. 3, pp. 279–311, 1966. DOI: 10.1007/BF02289464.</unstructured_citation>
          </citation>
          <citation key="ref45">
            <unstructured_citation>P. M. Kroonenberg and J. De Leeuw, “Principal component analysis of three-mode data by means of alternating least squares algorithms,” Psychometrika, vol. 45, no. 1, pp. 69–97, 1980. DOI: 10.1007/BF02293599.</unstructured_citation>
          </citation>
          <citation key="ref46">
            <unstructured_citation>L. Grasedyck, D. Kressner, and C. Tobler, “A literature survey of low-rank tensor approximation techniques,” GAMM-Mitteilungen, vol. 36, no. 1, pp. 53–78, 2013. DOI: 10.1002/gamm.201310004.</unstructured_citation>
          </citation>
          <citation key="ref47">
            <unstructured_citation>B. W. Bader and T. G. Kolda, “Efficient MATLAB computations with sparse and factored tensors,” SIAM Journal on Scientific Computing, vol. 30, no. 1, pp. 205–231, 2007. DOI: 10.1137/060676489.</unstructured_citation>
          </citation>
          <citation key="ref48">
            <unstructured_citation>I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov, “Linear algebra for tensor problems,” Computing, vol. 85, no. 3, pp. 169–188, 2009. DOI: 10.1007/s00607-009-0047-6.</unstructured_citation>
          </citation>
          <citation key="ref49">
            <unstructured_citation>G. Zhou, A. Cichocki, Q. Zhao, and S. Xie, “Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 4990–5003, 2015. DOI: 10.1109/TIP.2015.2478396.</unstructured_citation>
          </citation>
          <citation key="ref50">
            <unstructured_citation>V. de Silva and L.-H. Lim, “Tensor rank and the ill-posedness of the best low-rank approximation,” SIAM Journal on Matrix Analysis and Applications, vol. 30, no. 3, pp. 1084–1127, 2008. DOI: 10.1137/06066518X.</unstructured_citation>
          </citation>
          <citation key="ref51">
            <unstructured_citation>C. Hillar and L.-H. Lim, “Most tensor problems are NP-hard,” Journal of the ACM, vol. 60, no. 6, pp. 1–39, 2013. DOI: 10.1145/2512329.</unstructured_citation>
          </citation>
          <citation key="ref52">
            <unstructured_citation>A. Smilde, R. Bro, and P. Geladi, Multi-way Analysis: Applications in the Chemical Sciences. Chichester, UK: John Wiley &amp; Sons, 2004. DOI: 10.1002/0470012110.</unstructured_citation>
          </citation>
          <citation key="ref53">
            <unstructured_citation>A. Cichocki, R. Zdunek, and S. Amari, “Nonnegative Matrix and Tensor Factorization” [Lecture Notes], IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 142–145, 2008. DOI: 10.1109/MSP.2008.4408452.</unstructured_citation>
          </citation>
          <citation key="ref54">
            <unstructured_citation>X. Fu, K. Huang, N. D. Sidiropoulos, and W.-K. Ma, “Non-negative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications,” IEEE Signal Processing Magazine, vol. 36, no. 2, pp. 59–80, 2019. DOI: 10.1109/MSP.2018.2877582.</unstructured_citation>
          </citation>
          <citation key="ref55">
            <unstructured_citation>W. Hackbusch, Tensor Spaces and Numerical Tensor Calculus, 2nd ed. Cham, Switzerland: Springer, 2012. DOI: 10.1007/978-3-642-28027-6.</unstructured_citation>
          </citation>
          <citation key="ref56">
            <unstructured_citation>L. Qi and Z. Luo, Tensor Analysis: Spectral Theory and Special Tensors. Philadelphia, PA, USA: SIAM, 2017. DOI: 10.1137/1.9781611974751.</unstructured_citation>
          </citation>
          <citation key="ref57">
            <unstructured_citation>D. Kressner and R. Luce, “Fast Computation of the Matrix Exponential for a Toeplitz Matrix,” SIAM Journal on Matrix Analysis and Applications, vol. 39, no. 1, pp. 23–47, 2018. DOI: 10.1137/16M1083633.</unstructured_citation>
          </citation>
          <citation key="ref58">
            <unstructured_citation>C. F. Caiafa and A. Cichocki, “Generalizing the column–row matrix decomposition to multi-way arrays,” Linear Algebra and its Applications, vol. 433, no. 3, pp. 557–573, 2010. DOI: 10.1016/j.laa.2010.03.020.</unstructured_citation>
          </citation>
          <citation key="ref59">
            <unstructured_citation>S. Friedland, V. Mehrmann, R. Pajarola, and S. K. Suter, “On best rank one approximation of tensors,” Numerical Linear Algebra with Applications, vol. 20, no. 6, pp. 942–955, 2013. DOI: 10.1002/nla.1878.</unstructured_citation>
          </citation>
          <citation key="ref60">
            <unstructured_citation>J. Chen, W. Li, and Q. Luo, “Low-rank tensor completion with non-local self-similarity and multidimensional learnable transforms,” Inverse Problems, vol. 41, no. 6, p. 065010, Jun. 2025. DOI: 10.1088/1361-6420/addb69.</unstructured_citation>
          </citation>
          <citation key="ref61">
            <unstructured_citation>J. Kileel, T. G. Kolda, R. Jin, and R. Ward, “Scalable symmetric Tucker tensor decomposition,” SIAM Journal on Matrix Analysis and Applications, vol. 45, no. 4, pp. 1902-1927, Dec. 2024. DOI: 10.1137/23M1582928.</unstructured_citation>
          </citation>
          <citation key="ref62">
            <unstructured_citation>L.-H. Lim, “Singular values and eigenvalues of tensors: a variational approach,” in Proceedings of the 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP ’05), Puerto Vallarta, Mexico, 2005, pp. 129–132. DOI: 10.1109/CAMAP.2005.1574201.</unstructured_citation>
          </citation>
          <citation key="ref63">
            <unstructured_citation>P. Comon, “Tensor decompositions: state of the art and applications,” in Mathematics in Signal Processing V, J. G. McWhirter and I. K. Proudler, Eds. Oxford, UK: Oxford University Press, 2002, pp. 1–24. ISBN: 978-0198507345. Available: https://arxiv.org/abs/0905.0 454. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref64">
            <unstructured_citation>G. H. Golub and C. F. Van Loan, Matrix Computations, 4th ed. Baltimore, MD, USA: Johns Hopkins University Press, 2013. ISBN: 978-1421407944. Available: https://book s.google.com.tr/books/about/Matrix _Computations.html?id=X5YfsuCWpxMC. [Accessed: Jan. 27, 2026].</unstructured_citation>
          </citation>
          <citation key="ref65">
            <unstructured_citation>N. Li and B. Li, “Tensor Completion for On-Board Compression of Hyperspectral Images,” in Proceedings of the IEEE International Conference on Image Processing (ICIP), Hong Kong, China, 2010, pp. 4404–4407. DOI: 10.1109/ICIP.2010.5651225.</unstructured_citation>
          </citation>
        </citation_list>
      </journal_article>
    </journal>
  </body>
</doi_batch>