Advanced Sparse Estimation Methods: With a Focus on Missing Data Analysis and Transfer Learning

DOI
  • Takada Masaaki
    株式会社東芝 研究開発センター 知能化システム研究所 システムAIラボラトリー 統計数理研究所 ものづくりデータ科学研究センター

Bibliographic Information

Other Title
  • 発展的なスパース推定法—欠測データ分析と転移学習を中心として—

Description

<p>Sparse estimation is widely used in data science as a parameter estimation method for high-dimensional data. However, in real-world data and problems, Lasso and other basic methods may not provide sufficient accuracy, computational efficiency, and stability. In this paper, we introduce recent developments in sparse estimation methods for real-world complex and difficult problems, with a particular focus on missing data analysis and transfer learning.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390297372147236480
  • DOI
    10.11329/jjssj.53.69
  • ISSN
    21891478
    03895602
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

Report a problem

Back to top