The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam kertas ini, kami mencadangkan kaedah untuk memilih n-mod vektor tunggal dalam penguraian nilai tunggal peringkat tinggi. Kami memilih bilangan minimum n-mod vektor tunggal, apabila sempadan atas fungsi kos kuasa dua terkecil diambang. Yang dikurangkan n-pangkat semua mod tensor tertentu ditentukan secara automatik dan tensor diwakili dengan bilangan dimensi minimum. Kami menggunakan kaedah pemilihan untuk penghampiran peringkat rendah serentak bagi matriks. Keputusan eksperimen menunjukkan keberkesanan n-mod kaedah pemilihan vektor tunggal.
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Salinan
Kohei INOUE, Kiichi URAHAMA, "n-Mode Singular Vector Selection in Higher-Order Singular Value Decomposition" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 11, pp. 3380-3384, November 2008, doi: 10.1093/ietfec/e91-a.11.3380.
Abstract: In this paper, we propose a method for selecting n-mode singular vectors in higher-order singular value decomposition. We select the minimum number of n-mode singular vectors, when the upper bound of a least-squares cost function is thresholded. The reduced n-ranks of all modes of a given tensor are determined automatically and the tensor is represented with the minimum number of dimensions. We apply the selection method to simultaneous low rank approximation of matrices. Experimental results show the effectiveness of the n-mode singular vector selection method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.11.3380/_p
Salinan
@ARTICLE{e91-a_11_3380,
author={Kohei INOUE, Kiichi URAHAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={n-Mode Singular Vector Selection in Higher-Order Singular Value Decomposition},
year={2008},
volume={E91-A},
number={11},
pages={3380-3384},
abstract={In this paper, we propose a method for selecting n-mode singular vectors in higher-order singular value decomposition. We select the minimum number of n-mode singular vectors, when the upper bound of a least-squares cost function is thresholded. The reduced n-ranks of all modes of a given tensor are determined automatically and the tensor is represented with the minimum number of dimensions. We apply the selection method to simultaneous low rank approximation of matrices. Experimental results show the effectiveness of the n-mode singular vector selection method.},
keywords={},
doi={10.1093/ietfec/e91-a.11.3380},
ISSN={1745-1337},
month={November},}
Salinan
TY - JOUR
TI - n-Mode Singular Vector Selection in Higher-Order Singular Value Decomposition
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3380
EP - 3384
AU - Kohei INOUE
AU - Kiichi URAHAMA
PY - 2008
DO - 10.1093/ietfec/e91-a.11.3380
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2008
AB - In this paper, we propose a method for selecting n-mode singular vectors in higher-order singular value decomposition. We select the minimum number of n-mode singular vectors, when the upper bound of a least-squares cost function is thresholded. The reduced n-ranks of all modes of a given tensor are determined automatically and the tensor is represented with the minimum number of dimensions. We apply the selection method to simultaneous low rank approximation of matrices. Experimental results show the effectiveness of the n-mode singular vector selection method.
ER -