書誌事項
- タイトル別名
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- A Method of Peculiarity Factor Approximation using Vector Quantization Model
- ベクトル リョウシカ モデル ニ ヨル Peculiarity Factor ノ キンジ ケイサン
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The purpose of this research is to reduce the computational complexity of the Peculiarity Factor (PF). Recently, PF has been adopted as the index for anomaly data detection, and it is widely used in various mining scenes. The fact that PF has become a powerful mining tool is positive because its calculation method is extremely simple and the results of the calculation are easy to understand visually. One of the most important problems for using PF for large-scale data is the rapidly increasing computational complexity required when the data volume increases. The computational complexity of PF is in the polynomial order because the PF of each data is calculated distantly over all the data. In this study, we propose an approximation methodology for PF for computational reduction and for enhanced robustness using the vector quantization model. Approximate values of PF are calculated by replacing the actual data with the nodes of vector quantization model. By calculating PF based on the vector quantization node vectors, we achieve restraint in the increasing computational complexity.
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) 135 (3), 304-311, 2015
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204607897984
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- NII論文ID
- 130004870147
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 026245399
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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
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可