Abstract
Non-negative matrix factorization is an important method in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One of its significant drawback lies in its computational complexity. In this paper, we discuss a novel method to allow fast approximate transformation from input space to feature space defined by non-negative matrix factorization.
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Krömer, P., Platoš, J., Snášel, V. (2010). Fast Dimension Reduction Based on NMF. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_43
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DOI: https://doi.org/10.1007/978-3-642-16493-4_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
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