Online Canonical Polyadic Decomposition: Application of Fluorescence Tensors with Nonnegative Orthogonality and Sparse Constraint
The canonical polyadic decomposition (CPD) is now widely used in signal processing to decompose multi-way arrays. In many applications, it is important to add constraints to quickly converge on an optimal solution. In contrast to classical CPD, we then focus on online CPD. In this context, the numbe...
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| Autores principales: | , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2024
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/86755 |
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| Sumario: | The canonical polyadic decomposition (CPD) is now widely used in signal processing to decompose multi-way arrays. In many applications, it is important to add constraints to quickly converge on an optimal solution. In contrast to classical CPD, we then focus on online CPD. In this context, the number of relevant factors is usually unknown and can vary with time. We propose two algorithms to compute the online CPD based on sparse dictionary learning. We also introduce an application example in environmental sciences and evaluate the performances of the proposed approaches in this context on real data. |
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| ISBN: | 978-1-83769-180-7 |
| DOI: | 10.5772/intechopen.110123 |