Non-negative Matrix Factorizationを用いたベクトル空間情報検索モデルの次元削減手法

DOI Web Site Web Site 被引用文献2件 参考文献18件

書誌事項

タイトル別名
  • Information Retrieval Using Non-negative Matrix Factorization
  • Non negative Matrix Factorization オ モチイタ ベクトル クウカン ジョウホウ ケンサク モデル ノ ジゲン サクゲン シュホウ

この論文をさがす

説明

The Vector Space Model (VSM) is a conventional information retrieval model, which represents a document collection by a term-by-document matrix. Since term-by-document matrices are usually high-dimensional and sparse, they are susceptible to noise and are also difficult to capture the underlying semantic structure. Dimensionality reduction is a way to overcome these problems. Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are popular techniques for dimensionality reduction based on matrix decomposition, however they contain both positive and negative values in the decomposed matrices. In the work described here, we use Non-negative Matrix Factorization (NMF) for dimensionality reduction of the vector space model. Since matrices decomposed by NMF only contain non-negative values, the original data are represented by only additive, not subtractive, combinations of the basis vectors. This characteristic of parts-based representation is appealing because it reflects the intuitive notion of combining parts to form a whole. Using MEDLINE collection, we experimentally showed that NMF offers great improvement over the vector space model.

収録刊行物

被引用文献 (2)*注記

もっと見る

参考文献 (18)*注記

もっと見る

関連プロジェクト

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ