Global Noise Estimation Based on Tensor Product Expansion with Absolute Error

Akitoshi ITAI; Hiroshi YASUKAWA; Ichi TAKUMI; Masayasu HATA

doi:10.1093/ietfec/e90-a.4.778

Global Noise Estimation Based on Tensor Product Expansion with Absolute Error

Akitoshi ITAI, Hiroshi YASUKAWA, Ichi TAKUMI, Masayasu HATA

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This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where absolute error is used as the error function.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.4 pp.778-783

Publication Date: 2007/04/01

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Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e90-a.4.778

Type of Manuscript: Special Section PAPER (Special Section on Selected Papers from the 19th Workshop on Circuits and Systems in Karuizawa)

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Akitoshi ITAI, Hiroshi YASUKAWA, Ichi TAKUMI, Masayasu HATA, "Global Noise Estimation Based on Tensor Product Expansion with Absolute Error" in IEICE TRANSACTIONS on Fundamentals, vol. E90-A, no. 4, pp. 778-783, April 2007, doi: 10.1093/ietfec/e90-a.4.778.
Abstract: This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where absolute error is used as the error function.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.4.778/_p

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@ARTICLE{e90-a_4_778,
author={Akitoshi ITAI, Hiroshi YASUKAWA, Ichi TAKUMI, Masayasu HATA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Global Noise Estimation Based on Tensor Product Expansion with Absolute Error},
year={2007},
volume={E90-A},
number={4},
pages={778-783},
abstract={This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where absolute error is used as the error function.},
keywords={},
doi={10.1093/ietfec/e90-a.4.778},
ISSN={1745-1337},
month={April},}

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TY - JOUR
TI - Global Noise Estimation Based on Tensor Product Expansion with Absolute Error
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 778
EP - 783
AU - Akitoshi ITAI
AU - Hiroshi YASUKAWA
AU - Ichi TAKUMI
AU - Masayasu HATA
PY - 2007
DO - 10.1093/ietfec/e90-a.4.778
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2007
AB - This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where absolute error is used as the error function.
ER -