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Big Data Analysis of Weather Condition and Air Quality on Cosmetics Marketing
Zebin Wang,Tong Wu,Xinshuang Zhao,Shuchun Cheng,Genghui Dai,Weihui Dai 한국데이타베이스학회 2017 Journal of information technology applications & m Vol.24 No.3
Demands of cosmetics are affected not only by the well-known elements such as brand, price, and customer"s consumption capacity, but also by some latent factors, for example, weather and air environment. Due to complexity and dynamic changes of the above factors, their influences can hardly be estimated in an accurate way by the traditional approaches such as survey and questionnaires. Through modeling and statistical analysis of big data, this article studied the impacts of weather condition and air quality on customer flow and sales of the cosmetics distributors in China, and found several hidden influencing factors. It provided a big-data based method for the analysis of unconventional factors on cosmetics marketing in the changing weather condition and air environment.
Big Data Analysis of Weather Condition and Air Quality on Cosmetics Marketing
Wang, Zebin,Wu, Tong,Zhao, Xinshuang,Cheng, Shuchun,Dai, Genghui,Dai, Weihui Korea Data Strategy Society 2017 Journal of information technology applications & m Vol.24 No.3
Demands of cosmetics are affected not only by the well-known elements such as brand, price, and customer's consumption capacity, but also by some latent factors, for example, weather and air environment. Due to complexity and dynamic changes of the above factors, their influences can hardly be estimated in an accurate way by the traditional approaches such as survey and questionnaires. Through modeling and statistical analysis of big data, this article studied the impacts of weather condition and air quality on customer flow and sales of the cosmetics distributors in China, and found several hidden influencing factors. It provided a big-data based method for the analysis of unconventional factors on cosmetics marketing in the changing weather condition and air environment.
Hyperspectral Image Restoration Using Low-Rank Representation on Spectral Difference Image
Le Sun,Byeungwoo Jeon,Yuhui Zheng,Zebin Wu IEEE 2017 IEEE geoscience and remote sensing letters Vol.14 No.7
<P>This letter presents a novel mixed noise (i.e., Gaussian, impulse, stripe noises, or dead lines) reduction method for hyperspectral image (HSI) by utilizing low-rank representation (LRR) on spectral difference image. The proposed method is based on the assumption that all spectra in the spectral difference space of HSI lie in the same low-rank subspace. The LRR on the spectral difference space was exploited by nuclear norm of difference image along the spectral dimension. It showed great potential in removing structured sparse noise (e.g., stripes or dead lines located at the same place of each band) and heavy Gaussian noise. To simultaneously solve the proposed model and reduce computational load, alternating direction method of multipliers was utilized to achieve robust reconstruction. The experimental results on both simulated and real HSI data sets validated that the proposed method outperformed many state-of-the-art methods in terms of quantitative assessment and visual quality.</P>