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
본 논문에서는 선택하는 방법을 제안한다. n-고차 특이값 분해의 모드 특이 벡터. 우리는 최소 개수를 선택합니다. n-모드 특이 벡터, 최소 제곱 비용 함수의 상한이 임계값일 때. 감소된 n- 주어진 텐서의 모든 모드의 순위는 자동으로 결정되며 텐서는 최소 차원 수로 표현됩니다. 우리는 행렬의 동시 낮은 순위 근사에 선택 방법을 적용합니다. 실험 결과는 n-모드 특이 벡터 선택 방법.
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부
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
부
@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},}
부
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 -