書誌事項
- タイトル別名
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- Fast Block Coordinate Descent for Sparse Group Lasso
説明
<p>Sparse Group Lasso is a method of linear regression analysis that finds sparse parameters in terms of both feature groups and individual features. Block Coordinate Descent is a standard approach to obtain the parameters of Sparse Group Lasso, and iteratively updates the parameters for each parameter group. However, as an update of only one parameter group depends on all the parameter groups or data points, the computation cost is high when the number of the parameters or data points is large. This paper proposes a fast Block Coordinate Descent for Sparse Group Lasso. It efficiently skips the updates of the groups whose parameters must be zeros by using the parameters in one group. In addition, it preferentially updates parameters in a candidate group set, which contains groups whose parameters must not be zeros. Theoretically, our approach guarantees the same results as the original Block Coordinate Descent. Experiments show that our method enhances the efficiency of original method and achieves the same prediction error.</p>
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 36 (1), A-JB1_1-11, 2021-01-01
一般社団法人 人工知能学会
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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
- 1390005506388453760
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- NII論文ID
- 130007965424
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- ISSN
- 13468030
- 13460714
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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
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可