RI: Small: Spectral Methods for Learning Time Series and Graphical Models

研究人员研究了一类新的统计方法，用于学习时间序列和图形模型。他们的方法是基于谱分析和矩阵分解方法，在应用中取得了巨大的成功，但他们在图形模型中的使用引起了较少的关注。这项研究的目标是将矩阵分解方法的巨大成功扩展到更复杂的时间序列和某些图形模型领域，这将导致新的统计机器学习算法具有重要的实际应用。在信息时代，计算机智能的一个重要衡量标准是能够分析大量的电子数据，并在不确定的环境下做出关键决策。统计机器学习是分析电子数据的主要技术，图形模型是计算机系统和人类操作员理解这些复杂数据的数学工具，以促进决策制定。然而，用于学习图形模型的传统算法具有限制现代计算系统的能力的局限性。目前的研究尝试了一类新的数学算法，可用于设计更有效的图形模型，从而使现代计算机能够更准确地分析数据并实现更高的智能水平。

The investigators study a new class of statistical methods for learning time series and graphical models. Their approach is based on spectral analysis and matrix decomposition methods that have enjoyed tremendous success in applications, but their use in graphical models has drawn less attention. The goal of this investigation is to extend the enormous previous successes of matrix decomposition methods to the realm of more complicated time series and certain graphical models, which will lead to new statistical machine learning algorithms with important practical applications. In the information age, an important measure of computer intelligence is the ability to analyze huge amount of data that become available electronically, and make critical decisions under uncertain environment. Statistical machine learning is the main technique for analyzing electronic data, and graphical models are mathematical tools for understanding these complex data both by computer systems and by human operators in order to facilitate decision making. However, traditional algorithms for learning graphical models have limitations that restrict capabilities of modern computing systems. The current research attempts a new class of mathematical algorithms that can be used to design more effective graphical models, which in turn allows modern computers to analyze data more accurately and achieve higher level of intelligence.