Document Classification with Varied Viewpoints Using Matrix Decomposition

The result of document classification is not unique and there can be many other results from the different perspectives. In other words, the classification results can vary according to user´s viewpoints of classification. If a document classification system ignores the user´s viewpoints, the result of classification will be different from the result desired by the user and the difference between the user´s desired result and the system´s result can cause some inhibitions and oversights in information retrieval. So, we extract the user´s viewpoints from the classification examples performed preliminarily by the user and use them to the following classification in order to reflect the user´s desire. In this paper, we propose four methods to extract viewpoints and three methods to classify documents using matrix decomposition such as Nonnegative Matrix Factorization (NMF). We show the results of comparative experiments with the original NMF, Semi-Supervised NMF (SSNMF) and our proposed methods.