CIF: Small: Sensing-Aware Decision Making for High-Dimensional Signals

CIF：小：高维信号的传感感知决策

• 批准号: 1218992
• 负责人: William Karl
• 金额: $ 50万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218992&HistoricalAwards=false
• 关键词: CIF; Small; Sensing; Aware; Decision

There has been an explosion in our ability to sense and record the world around us. This has led to new discoveries and allowed us to consider new paradigms in nearly every walk of life. While the promise of these developments is significant, the explosion of sensing has also created substantial challenges. These challenges include high-dimensionality of observations and the associated ``curse of dimensionality'', non-trivial relationships between the observations and the latent variables we care about, poor understanding of models, and lack of sufficient training data from which to learn these models. Motivated by such problems, this research studies an approach which we term ``sensing-aware inferencing'' that leverages knowledge about the underlying structure of the sensing process for data-driven inferencing. Such problems are at the frontier of statistical signal processing and advance this frontier by contributing to signal processing theory and practice.This research involves both analysis of the fundamental limits to the performance of, as well as the development of new methods for, decision making and inference from high-dimensional data when the data are related to latent variables through a underlying sensing structure. The methods being developed explicitly acknowledge and account for the underlying sensing process in a unified and optimal way. Research is being advanced along two major thrusts: 1) theoretical investigation of the fundamental limits of classification performance as dimension grows faster than available training data under various states of sensing knowledge, 2) development of methods for inference in high-dimensional problems exploiting sensing structure in a unified way. Throughout, the samples available for such learning are severely limited relative to the dimensionality of the observations.

我们感知和记录周围世界的能力出现了爆炸式的增长。这导致了新的发现，并使我们能够考虑几乎各行各业的新范式。虽然这些发展的前景是显著的，但传感的爆炸也带来了巨大的挑战。这些挑战包括观察的高维性和相关的“维数灾难”，观察和我们关心的潜在变量之间的非平凡关系，对模型的理解不足，以及缺乏足够的训练数据来学习这些模型。出于这样的问题，本研究研究的方法，我们称之为“感知感知推理”，利用知识的基础结构的感知过程中的数据驱动的推理。这些问题是在统计信号处理的前沿，并通过促进信号处理理论和practice.This研究涉及到的性能的基本限制的分析，以及新方法的发展，决策和推理高维数据时，数据是相关的潜在变量通过一个基本的传感结构。正在开发的方法明确承认并以统一和最佳的方式解释基本的传感过程。研究正在推进沿着两个主要的推力：1）的基本限制的分类性能的理论研究，作为维度增长速度比现有的训练数据下的各种状态的传感知识，2）开发的方法，在高维问题的推理，利用传感结构在一个统一的方式。在整个过程中，可用于这种学习的样本相对于观察的维度是非常有限的。