EAPSI: Developing Fast and Accurate Methods for Grouping Objects in a Dataset Using Inconsistent Labels

EAPSI:开发快速准确的方法,使用不一致的标签对数据集中的对象进行分组

基本信息

  • 批准号:
    1613938
  • 负责人:
  • 金额:
    $ 0.54万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Fellowship Award
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-06-15 至 2017-05-31
  • 项目状态:
    已结题

项目摘要

In today's information-rich age, it is relatively easy to collect large amounts of data when attempting to solve problems and answer questions in almost any field. It is often very challenging, though, to analyze and extract useful information from these datasets, due to their massive size and lack of organization. This project will investigate special techniques for organizing a dataset into groups of similar objects to allow for easier analysis. This task is called clustering, and can be applied in vastly different settings, such as the study of protein interactions in biology, or the categorization of webpages in a large database. The research will be conducted at the University of Melbourne in collaboration with Professor Anthony Wirth. Dr. Wirth is an expert in data analysis and a pioneer in the study of "clustering with advice," a technique for clustering data when the only available information is a list of inconsistent labels that mark data points as "similar" or "dissimilar." In general, exactly solving this clustering problem on a large dataset is slow and computationally expensive. This research aims to explore basic assumptions on the input dataset that can lead to more efficient methods for clustering with inconsistent labels. Developing faster methods for this problem will expand current theoretical understanding of clustering with advice, as well as making this useful technique more achievable in practice.The researcher will apply techniques in integer-constrained linear programming and numerical linear algebra to obtain a solution for the clustering with advice problem when the input data can be represented as a low-rank matrix. The primary objective is to develop a polynomial time algorithm for solving the problem for rank-2 matrices and prove complexity results about this special case of the problem. Obtaining a fast solution under low-rank assumptions sheds light on when an otherwise hard problem becomes tractable in practice, and will stimulate research in showing similar results for other difficult problems.This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the Australia Academy of Science.
在当今这个信息丰富的时代,在试图解决几乎任何领域的问题和回答问题时,收集大量数据相对容易。然而,由于其庞大的规模和缺乏组织,从这些数据集中分析和提取有用的信息通常是非常具有挑战性的。本项目将研究将数据集组织成相似对象组以便于分析的特殊技术。这项任务被称为聚类,可以应用于非常不同的环境中,例如研究生物学中的蛋白质相互作用,或者对大型数据库中的网页进行分类。这项研究将在墨尔本大学与安东尼·沃斯教授合作进行。沃斯博士是数据分析专家,也是“建议聚类”研究的先驱。“建议聚类”是一种在唯一可用的信息是将数据点标记为“相似”或“不相似”的不一致标签列表的情况下对数据进行聚类的技术。一般来说,在大型数据集上准确地解决这个集群问题是很慢的,而且计算成本很高。这项研究的目的是探索对输入数据集的基本假设,以导致更有效的方法来对不一致的标签进行聚类。研究人员将利用整数约束线性规划和数值线性代数中的技术来解决输入数据可以表示为低秩矩阵时的带建议聚类问题。主要目的是给出一种多项式时间算法来求解二阶矩阵问题,并证明了这一特殊情形的复杂性结果。在低等级假设下获得快速解决方案有助于揭示在实践中原本很难解决的问题何时变得容易解决,并将刺激对其他困难问题显示类似结果的研究。东亚和太平洋夏季学院项目下的这个奖项支持一名美国研究生的暑期研究,由NSF和澳大利亚科学院联合资助。

项目成果

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