Abstract
Matrix decompositions are used for many data mining purposes. One of these purposes is to find a concise but interpretable representation of a given data matrix. Different decomposition formulations have been proposed for this task, many of which assume a certain property of the input data (e.g., nonnegativity) and aim at preserving that property in the decomposition.
This is an extended abstract of an article published in the Data Mining and Knowledge Discovery journal [1].
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Miettinen, P.: The Boolean column and column-row matrix decompositions. Data Mining and Knowledge Discovery 17(1), 39–56 (August 2008)
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© 2008 Springer-Verlag Berlin Heidelberg
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Miettinen, P. (2008). The Boolean Column and Column-Row Matrix Decompositions. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87479-9_15
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DOI: https://doi.org/10.1007/978-3-540-87479-9_15
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