کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9653392 | 679728 | 2005 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Unsupervised learning with stochastic gradient
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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-T0S, with imposed thermodynamics constraint at a constant temperature T0 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 68, October 2005, Pages 130-160
Journal: Neurocomputing - Volume 68, October 2005, Pages 130-160
نویسندگان
Harold Szu, Ivica Kopriva,