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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Big data analytics enabled by feature extraction based on partial independence
ترجمه فارسی عنوان مقاله:
تحلیل داده های بزرگ فعال شده توسط قابلیت استخراج بر اساس استقلال جزئی
منبع:
Sciencedirect - Elsevier - Neurocomputing, 288 (2018) 3-10: doi:10:1016/j:neucom:2017:07:072
نویسنده:
Qiao Ke a, Jiangshe Zhang a,∗, Houbing Song b, Yan Wan c
چکیده انگلیسی:
Complex cells in primary visual cortex (V1) selectively respond to bars and edges at a particular location
and orientation. Namely, they are relatively invariant to the phase as well as selective to the frequency
and orientation emerging from natural images that are analogous to the characteristics of complex cells
in V1 with the energy function of receptive fields (RFs) from tuning curve test with sinusoidal function in
our related jobs. In this paper, we propose a feature learning algorithm based on the overcomplete AISA
to apply on big data in parallel computing. In order to demonstrate the effectiveness of the overcomplete
AISA features in the classification task, two feature representation architectures are evolved into the par
tial independent signal bases and partial independent factorial representation, respectively. Experiments
on four datasets (Coil20, Extended YaleB, USPS, PIE), acquired conjunction with two classification archi
tectures based on the overcomplete AISA features, show that the classification accuracy is mostly higher
than those obtained from the other ICA related features and two other sparse representation features
with a small number of training samples via nearest neighbor (NN) classification method.
Keywords: Independent Component(IC) ، Overcomplete features ، Sparse representation ، Big data
قیمت: رایگان
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