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Complete dictionary type stochastic sparse representation and its applications in random process simulation
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DOI：10.1080/15326349.2026.2652009.png)
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Abstract
En 中文
This paper presents a sparse representation method for stochastic signals based on the complete dictionary constructed from the Szeg & odblac; kernel. The study extends the theoretical framework proposed by Qian et al. By designing a complete dictionary of the Szeg & odblac; kernel, we achieve an adaptive sparse expansion of stochastic signals. A rigorous convergence theorem is established, and a practical algorithm is developed. The proposed methodology is further applied to numerical simulations of stochastic processes, demonstrating significant advantages in the representation of random signals.
Keywords:
Complete dictionary
Szeg & odblac
kernel
stochastic sparse representation
random process simulation
Journal
S
IF:
0.7
Papers:
15
Citations:
0