奇异值分解
计算机科学
潜在语义分析
情报检索
搜索引擎索引
基础(线性代数)
集合(抽象数据类型)
余弦相似度
向量空间模型
期限(时间)
基质(化学分析)
人工智能
算法
数据挖掘
模式识别(心理学)
数学
物理
几何学
材料科学
量子力学
复合材料
程序设计语言
作者
Scott Deerwester,Susan T. Dumais,George W. Furnas,Thomas K. Landauer,Richard A. Harshman
出处
期刊:Journal of the American Society for Information Science
[Wiley]
日期:1990-09-01
卷期号:41 (6): 391-407
被引量:11353
标识
DOI:10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9
摘要
A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. Initial tests find this completely automatic method for retrieval to be promising. © 1990 John Wiley & Sons, Inc.
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