Models for High Dimensional Mixed Regression
| 项目名称： | Models for High Dimensional Mixed Regression |
| 摘要： | The project team proposes to consider the mixed regression problem in high dimensions, under adversarial and stochastic noise. The team will consider convex optimization-based formulations with the aim of showing that it provably recovers the true solution. This agenda will seek to provide upper bounds on the recovery errors for both arbitrary noise and stochastic noise settings. The project team also will seek matching minimax lower bounds (up to log factors), showing that under certain assumptions, their algorithm is information-theoretically optimal. The team's preliminary results represent the first (and currently only known) tractable algorithm guaranteeing successful recovery with tight bounds on recovery errors and sample complexity. Mixture models treat observed data as a superposition of simple statistical processes. Thus they are particularly relevant in the transportation setting, when city-wide phenomena are often mixtures of simple processes (cut-through traffic, intra-city movement, etc.). |
| 状态： | Completed |
| 资金： | 33000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Bhat, Chandra R |
| 执行机构： | Data-Supported Transportation Operations and Planning Center |
| 开始时间： | 20130930 |
| 预计完成日期： | 20160930 |
| 实际结束时间： | 20160930 |
| 主题领域： | Data and Information Technology；Passenger Transportation；Planning and Forecasting |
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