Dictionary learning by Normalized Bilateral Projection

• Item type: Trans(1)
• 公開日: 2016-03-31
• タイトル: Dictionary learning by Normalized Bilateral Projection
• タイトル / 言語: en
• タイトル: Dictionary learning by Normalized Bilateral Projection
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] dictionary learning, sparse coding, bilateral projections, image reconstruction
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: University of Tsukuba
• 著者所属(英): en
• 著者所属(英): University of Tsukuba
• 著者名: Taro, Tezuka
• 著者名(英): Taro, Tezuka
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Dictionary learning is an unsupervised learning task that finds a set of template vectors that expresses input signals by sparse linear combinations. There are currently several methods for dictionary learning, for example K-SVD and MOD. In this paper, a new dictionary learning method, namely K-normalized bilateral projections (K-NBP), is proposed, which uses faster low rank approximation. Experiments showed that the method was fast and when the number of iterations was limited, it outperforms K-SVD. This indicated that the method was particularly suited to large data sets with high dimension, where each iteration takes a long time. K-NBP was applied to an image reconstruction task where images corrupted by noise were recovered using a dictionary learned from other images.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.3(online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Dictionary learning is an unsupervised learning task that finds a set of template vectors that expresses input signals by sparse linear combinations. There are currently several methods for dictionary learning, for example K-SVD and MOD. In this paper, a new dictionary learning method, namely K-normalized bilateral projections (K-NBP), is proposed, which uses faster low rank approximation. Experiments showed that the method was fast and when the number of iterations was limited, it outperforms K-SVD. This indicated that the method was particularly suited to large data sets with high dimension, where each iteration takes a long time. K-NBP was applied to an image reconstruction task where images corrupted by noise were recovered using a dictionary learned from other images.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.3(online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 9, 号 1, 発行日 2016-03-31
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会