Big Data Classification Methods and Applications Based on Statistical Machine Learning and Convex Optimization

2015 Fiscal Year Final Research Report

• Project/Area Number: 25330045
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Statistical science
• Research Institution: Shonan Institute of Technology
• Principal Investigator: KOBAYASHI Manabu 湘南工科大学, 工学部, 教授 (80308204)
• Co-Investigator(Kenkyū-buntansha): HIRASAWA Shigeichi 早稲田大学, 理工学術院, 名誉教授 (30147946)
• Research Collaborator: YOSHIMOTO Masashi
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 学習理論 / 凸最適化 / 統計的モデル / ビッグデータ解析 / 隠れ属性モデル / メトリックラーニング / I-Scover

Outline of Final Research Achievements

Applying classification methods based on the statistical machine learning and convex optimization for big data, we showed that it was possible to obtain efficiently the high precision solutions for wide range of various problems.
Specifically, we proposed algorithms and analysis methods, and showed the effectiveness for the following problems:
(1)privacy preserving distributed calculation problem for the case which some parties have different secret data, (2)latent class model analysis problems of EC site or institutional research, (3)dynamic reconfiguration circuit design problem, (4)document classification problem based on L1 optimization, (5)lossless data compression using CART, (6)fault-diagnosis problem using markov random field, and (7)programming edit history acquisition and visualization problem for many students.

Free Research Field

学習理論