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Lower Dimensional Representation of Text Data for Efficient and Effective Information Retrieval

Lower Dimensional Representation of Text Data for Efficient and Effective Information Retrieval
用于高效且有效的信息检索的文本数据的低维表示
批准号:
0549253
负责人:
Haesun Park
金额:
$0.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-07-01 至 2006-06-30

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中文摘要
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英文摘要
ABSTRACT 0204109Haesun ParkUniversity of Minnesota- Twin CitiesDue to today's exponential growth of the internet and computing power, an information retrievalsystem is expected to handle a tremendous amount of data, and users demand more efficient techniquesto obtain useful information from the flood of data. The goal of this proposed research is to find lower dimensional representations of text data in vector space based information retrieval. Dimension reduction is imperative for achieving high efficiency and effectiveness in manipulating the massive quantity of data in today's information retrieval system. A problem of fundamental importance here is to achieve better representation of the data with relatively severe dimension reduction, rather than simple dimension reduction through a lower rank approximation of a matrix. One difficulty is that it is not easy to measure by a theoretical formula how well a certain dimension reduction method provides a good representation of the original data, and it will be essential to conduct theoretical research in parallel with experimental study.
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Collaborative Research: OAC Core: Robust, Scalable, and Practical Low Rank Approximation
  • 批准号:
    2106738
  • 项目类别:
    Standard Grant
  • 资助金额:
    $27.5万
  • 财政年份:
    2021
  • 负责人:
    Haesun Park
  • 依托单位:
SI2-SSE: Collaborative Research: High Performance Low Rank Approximation for Scalable Data Analytics
  • 批准号:
    1642410
  • 项目类别:
    Standard Grant
  • 资助金额:
    $33.23万
  • 财政年份:
    2016
  • 负责人:
    Haesun Park
  • 依托单位:
CAREER: New Representations of Probability Distributions to Improve Machine Learning --- A Unified Kernel Embedding Framework for Distributions
  • 批准号:
    1350983
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $49.97万
  • 财政年份:
    2014
  • 负责人:
    Haesun Park
  • 依托单位:
EAGER: Hierarchical Topic Modeling by Nonnegative Matrix Factorization for Interactive Multi-scale Analysis of Text Data
  • 批准号:
    1348152
  • 项目类别:
    Standard Grant
  • 资助金额:
    $17.5万
  • 财政年份:
    2013
  • 负责人:
    Haesun Park
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis