書誌事項
- タイトル別名
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- 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
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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
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- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
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電気学会論文誌Ｃ（電子・情報・システム部門誌） 124 (7), 1500-1506, 2004
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679580788480
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- NII論文ID
- 10013268306
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 7020378
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDLサーチ
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可