Document Classification using Matrix Decomposition with Varied Viewpoints

Kaname Maruta; Hidetoshi Nagai; Teigo Nakamura

doi:10.52731/iee.v1.i4.27

Kaname Maruta Kyushu Institute of Technology
Hidetoshi Nagai Kyushu Institute of Technology
Teigo Nakamura Kyushu Institute of Technology

DOI: https://doi.org/10.52731/iee.v1.i4.27

Keywords: Document Classification, NMF, Matrix Decomposition

Abstract

Classification results are not unique and can vary according to the user’s viewpoint. If a document classification system ignores the user’s viewpoints, classification will be different from the result desired by the user, and the difference between the user’s desired result and the system’s produced result can cause some inhibitions and oversights in information retrieval. Extracting the user’s viewpoints from the classification examples performed preliminarily by the user allows us to configure classifications that reflect the user’s desire. In this study, we propose four methods to extract viewpoints and three methods to classify documents using Nonnegative Matrix Factorization (NMF) matrix decomposition. We exhibit the results of comparative experiments with the original NMF, Semi-Supervised NMF (SSNMF) and our proposed methods.

Author Biography

Kaname Maruta, Kyushu Institute of Technology

Graduate School of Computer Science and Systems Engineering

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