Dimensionality Reduction of Vector Space Model for Information Retrieval using Simple Principal Component Analysis

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  • Simple PCAを用いたベクトル空間情報検索モデルの次元削減
  • Simple PCA 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

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In this paper, we propose to use the Simple Principal Component Analysis (SPCA) for dimensionality reduction of the vector space information retrieval model. The SPCA algorithm is a data-oriented fast method which does not require the computation of the variance-covariance matrix. In SPCA, principal components are estimated iteratively so we also propose a criteria to determine the convergence. The optimum number of iterations for each principal component can be determined using the criteria. Experimentally, we show that the SPCA-based method offers improvement over the conventional SVD-based method despite its small amount of computation. This advantage of SPCA can be attributed to its iterative procedure which is similar to clustering methods such as k-means clustering. On the other hand, the proposed method which orthogonalizes the basis vectors also achieved much higher accuracy than the conventional random projection method based on k-means clustering.

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