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Unsupervised learning with stochastic gradient
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DOI：10.1016/j.neucom.2004.11.010.png)
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摘要
En 中文
A stochastic gradient is formulated based on deterministic gradient augmented with Cauchy simulated annealing capable to reach a global minimum with a convergence speed significantly faster when simulated annealing is used alone. In order to solve space-time variant inverse problems known as blind source separation, a novel Helmholtz free energy contrast function, H = E - T(0)S, with imposed thermodynamics constraint at a constant temperature T(0) was introduced generalizing the Shannon maximum entropy S of the closed systems to the open systems having non-zero input-output energy exchange E. Here, only the input data vector was known while source vector and mixing matrix were unknown. A stochastic gradient was successfully applied to solve inverse space-variant imaging problems on a concurrent pixel-by-pixel basis with the unknown mixing matrix (imaging point spread function) varying from pixel to pixel. Published by Elsevier B.V.
Keyword:
stochastic optimization
Cauchy annealing
blind source separation
Helmholtz free energy
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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