期刊
INFORMATION PROCESSING & MANAGEMENT
卷 42, 期 2, 页码 373-386出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2004.11.005
关键词
nonnegative matrix factorization; text mining; conjugate gradient; constrained least squares
A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies. (c) 2004 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据