Due to the non-negativity of the matrix factors, Non-negative Matrix Factorization (NMF) is favorable for transforming a high-dimensional original Terms-Documents matrix into a lower-dimensional semantic Concepts-Documents matrix in the text categorization. With the iterative nature of all NMF algorithms, the NMF matrix factors need initializing. In this paper, we propose a clustering-based method for initializing the NMF according to the term vectors instead of the document vectors as the previous researches.
CITATION STYLE
Nam, L. N. H., & Quoc, H. B. (2017). The clustering-based initialization for non-negative matrix factorization in the feature transformation of the high-dimensional text categorization system: A viewpoint of term vectors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10450 LNCS, pp. 511–522). Springer Verlag. https://doi.org/10.1007/978-3-319-67008-9_40
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