Oblique Factor Model for Selecting Training Samples

YU Xin; ZHENG Zhaobao; LI Linyi

doi:10.13203/j.whugis20200631

Volume 42 Issue 11

Nov. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(11): 1870-1877. > DOI: 10.13203/j.whugis20200631

YU Xin, ZHENG Zhaobao, LI Linyi. Oblique Factor Model for Selecting Training Samples[J]. Geomatics and Information Science of Wuhan University, 2022, 47(11): 1870-1877. DOI: 10.13203/j.whugis20200631

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Oblique Factor Model for Selecting Training Samples

1.
Beijing Institute of Petrochemical Technology, Beijing 102617, China
2.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Funds:

The National Key Research and Development Program of China 2018YFC0407804

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Author Bio:
YU Xin, PhD, professor, specializes in photogrammetry and remote sensing, image interpretation and artificial intelligence. E-mail: china_yuxin@163.com
Corresponding author:
LI Linyi, PhD, associate professor. E-mail: lilinyi@whu.edu.cn
Received Date: December 09, 2020
Available Online: November 15, 2022
Published Date: November 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives Researchers notice that the quality of training samples will impact the effective of training phase and then further will have an influence on the overall classification accuracy in the testing phase. In fact, representativeness or typicalness of training samples is able to reflect the quality of training samples in a way. Especially for the currently popular deep learning methods, it has needed thousands or millions of training samples. Therefore, how to reduce the number of training samples for deep learning method becomes a very important problem. In another hand, from the actual application angle, it is also very expensive. Therefore, we propose one method of reducing the training samples as less as possible based on the representativeness or typicalness of training samples.
Methods Selection of training samples based on oblique factor model is proposed and it relaxes the independent condition among common factors in the orthogonal factor model, which is able to better describe the real world.
Results Experimental results show the proposed method is feasible and effective and it is able to select more representative training samples than the method of selection of training samples based on orthogonal factor model and achieve better performance in the overall classification precision and stability. And the selection of training samples based on oblique factor model outperforms selection of training samples based on orthogonal factor model. And the distribution of selected samples becomes more decentralized and reasonable and the overall classification accuracy averagely improves about 3%.
Conclusions The proposed method not only supports how to optimize capturing data in the theory, but also is able to guide how to effectively capture data in the actual application.
- oblique factor model,
- training samples,
- image classification,
- orthogonal factor model,
- samples selection

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References

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