Extraction of Dynamic Nonnegative Features from Multidimensional Nonstationary Signals
| Autor: | Rafal Zdunek, Michalina Kotyla |
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| Rok vydání: | 2016 |
| Předmět: |
Sequence
Computer science Feature extraction Extraction (chemistry) 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Algorithm Least squares Non-negative matrix factorization |
| Zdroj: | Data Mining and Big Data ISBN: 9783319409726 DMBD |
| DOI: | 10.1007/978-3-319-40973-3_57 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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