Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Local Geometric Structure Feature Learning

Shi, Guangyao; Hong, Huixiao; Wang, Lihua

doi:10.1109/lgrs.2019.2944970

Cited by 70 publications

(33 citation statements)

References 20 publications

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“…There are many dimensionality reduction methods, such as local neighborhood structure preserving embedding (LNSPE), local geometric structure Fisher analysis (LGSFA) and principal component analysis (PCA) [35], [36]. From the view of calculation simplicity, PCA is used in this paper to solve the above problem.…”

Section: B Principle Component Analysis(pca)mentioning

confidence: 99%

Fault Diagnosis Based on Space Mapping and Deformable Convolution Networks

Zhao

Zhou

et al. 2020

IEEE Access

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The data-driven method based on deep learning is one of the popular issues in the field of fault diagnosis. The completeness and representativeness of the feature matrix from massive and highdimensional fault data have a great impact on fault diagnosis performance. In addition, the ability of deep networks to extract the spatial characteristics between fault data is especially important for the accuracy of fault diagnosis. Therefore, we propose a method based on space mapping and deformable convolution networks (DCN) to ensure diagnostic accuracy by improving the spatial resolution and spatial constraint characteristics, and both the size and shape of the convolution kernel, one of the key steps in DCN, are adjusted adaptively according to the input of different sizes. Original data are projected into a more discriminative space by the combination of CN and PCA (i.e., space mapping). Then, DCN extract spatial constraints between fault data by training. The Case Western Reserve University (CWRU) bearing dataset and Xi'an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU-SY) datasets are used as benchmarks to perform experiments. The results demonstrate that the fault diagnosis method proposed in this paper performs well and can achieve 100% accuracy in the first several epochs. Comparative experiments based on 3 deep learning methods that combine preprocessed and unprocessed data with a convolutional neural network (CNN), residual networks (ResNets) and DCN are carried out to further show the advantages of the fault diagnosis method based on space mapping and DCN.

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Section: B Principle Component Analysis(pca)mentioning

confidence: 99%

Fault Diagnosis Based on Space Mapping and Deformable Convolution Networks

Zhao

Zhou

et al. 2020

IEEE Access

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“…This property can be considered as an advantage of the nonlinear method. However, it is noticeably that the nonuniform intensity distribution of the background may cause the difficulty of further optical information extraction [29,30].…”

Section: A Logical Image Transform and Logical Image Reconstructionmentioning

confidence: 99%

Logical Image Reconstruction by Monitoring a Seeded Potential in Variable Detection Planes

et al. 2020

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“…The domain adaptation technique makes the deep learningbased approach work on the target domain data, which has different distribution characteristics than the training data. Furthermore, the dimensionality reduction technology can further reduce the dimensionality of the input data, thereby reducing the computing cost [18], [19]. The introduction of domain adaptation methods and dimensionality reduction methods to our system will be further studied in future work.…”

Section: Introductionmentioning

confidence: 99%