Abstract
In the paper, we study the problem of time-varying feature extraction from a long sequence of dynamic multidimensional observations. Imposing the nonnegativity constrains onto the estimated features, the problem can be represented by an on-line nonnegative matrix factorization (NMF) model. To update the nonnegative factors in such a model, we used various computational strategies, including the row-action projections (Kaczmarz algorithm), rank-one least square updates, and modified proximal gradient iterations. The numerical experiments, performed on the benchmarks of nonstationary spectral signals, demonstrated that the Kaczmarz algorithm appeared to be the most efficient, both with respect to the performance and the computational time.
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Acknowledgments
This work was partially supported by the grant 2015/17/B/ ST6/01865 funded by National Science Center (NCN) in Poland. Calculations have been carried out in Wroclaw Centre for Networking and Supercomputing, grant no. 127.
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Zdunek, R., Kotyla, M. (2016). Extraction of Dynamic Nonnegative Features from Multidimensional Nonstationary Signals. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_57
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DOI: https://doi.org/10.1007/978-3-319-40973-3_57
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