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- 佐々木 亮平
- 東京工科大学コンピュータサイエンス学部
書誌事項
- タイトル別名
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- Mathematical Programming for Multidimensional Data Reconstruction and Its Progress
- タジゲン データ サイコウセイ ノ タメ ノ スウリ ケイカク ト ソノ シンテン
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抄録
<p>Research on reconstructing original data from data that can only be partially observed due to noise or missing data has been ongoing for many years. Such problems are generally referred to as matrix estimation problems. The problem can be formulated when the data to be estimated can be defined as matrix variables in the problem of reconstructing partially observed data. When the properties of the target matrix are unknown, a common approach is a method called matrix rank minimization, which is known for its high estimation accuracy in various fields such as audio, image, and wireless communication. However, this method assumes that the data belong to a linear subspace, and if this assumption is not satisfied, the estimation accuracy significantly deteriorates. Therefore, in recent years, this assumption has been extended to manifolds, and various methods based on this assumption have been proposed. This paper reviews the progress of these methods and describes the latest techniques in this field.</p>
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 144 (2), 43-46, 2024-02-01
一般社団法人 電気学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390017522234930048
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 033316536
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
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- 抄録ライセンスフラグ
- 使用不可