Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction

Qu, Hongchun; Li, Lin; Li, Zhaoni; Zheng, Jian

doi:10.1016/j.eswa.2021.115055

Cited by 28 publications

(12 citation statements)

References 26 publications

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“…Isomap preserves the global geometric features of the initial data and extracts features by reconstructing the underlying smooth manifold of HSI. It is nonlinear dimensionality reduction based on linear and multidimensional scaling transformation 18 . Isomap has been applied in image and HSI classification 19 , 20 , but there is no report on visible-NIR hyperspectral classification of grass.…”

Section: Methodsmentioning

confidence: 99%

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Zhao

Pan

Wei

et al. 2022

Sci Rep

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Grass community classification is the basis for the development of animal husbandry and dynamic monitoring of environment, which has become a critical problem to further strengthen the intelligent management of grassland. Compared with grass survey based on satellite remote sensing, the visible near infrared (NIR) hyperspectral not only monitor dynamically in a short distance, but also have high dimensions and detailed spectral information in each pixel. However, the hyperspectral labeled sample for classification is expensive and manual selection is more subjective. In order to solve above limitations, we proposed a visible-NIR hyperspectral classification model for grass based on multivariate smooth mapping and extreme active learning (MSM–EAL). Firstly, MSM is used to preprocess and reconstruct the spectrum. Secondly, by jointing XGBoost and active learning (AL), the advanced samples with the largest amount of information are actively selected to improve the performance of target classification. Innovation lies in: (1) MSM global enhanced preprocessing spectral reconstruction algorithm is proposed, in which isometric feature mapping is effectively applied to the grass hyperspectral for the first time. (2) EAL framework is constructed to solve the issue of high cost and small number for hyperspectral labeled samples, at the same time, enhance the physical essence behind spectral classification more intuitively. A field hyperspectral collection platform is assembled to establish nm resolution visible-NIR hyperspectral dataset of grass, Grass1, containing 750 samples, which to verify the effectiveness of the model. Experiments on the Grass1 dataset confirmed that compared with the full spectrum, the time consumption of MSM was reduced by 9.471 s with guaranteed overall accuracy (OA). Comparing EAL with AL, and other classification algorithms, EAL improves OA 22.2% over AL, and XAL has the best performance value on Kappa, Macro, Recall and F1-score, respectively. Altogether, the lightweight MSM–EAL model realizes intelligent and real-time classification, providing a new method for obtaining high-precision inter group classification of grass.

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Section: Methodsmentioning

confidence: 99%

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Zhao

Pan

Wei

et al. 2022

Sci Rep

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show abstract

“…Isomap is a non‐linear stream shape algorithm based on neighbourhood map and multidimensional scaling (MDS) analysis [20, 21], whose main idea is to estimate the global stream shape geodesic distance between each data point using local neighbourhood distance, and to replace the Euclidean distance by establishing the original inter‐data geodesic distance as a similarity measure between sample points that better reflects the true low‐dimensional structure of the manifold. The process of Isomap algorithm is as follows: Construct a neighbourhood graph G. Set the number of transformer sample data to n and the set of samples in high‐dimensional space to

X=\left\{{x}_{i}\vert i=1,2,\text{\ldots },n\right\},X\in {R}^{N}

.…”

Section: Transformer Fault Feature Extractionmentioning

confidence: 99%

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

Shi

et al. 2023

IET Electric Power Appl

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Oil‐immersed transformers play an important role in the stable operation of power systems. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on Isomap and IChOA‐LSSVM is proposed. Firstly, Isomap is used to reduce the dimensionality of the 14‐dimensional transformer fault characteristics data to eliminate redundant data between variables. In addition, IChOA is proposed to optimise the Least Squares Support Vector Machine (LSSVM) parameters and establish an optimal diagnosis model based on LSSVM. For ChOA, three improvement methods are proposed. Circle mapping is used instead of the original population initialisation to improve population diversity. In addition, the location weighting strategy with proportional weights and the Gaussian Corsi variation strategy is proposed to improve the optimization accuracy and efficiency of ChOA. The improved ChOA is compared with the original ChOA, PSO and GWO algorithms by five benchmark test functions. Finally, the improved Chimpanzee Optimisation algorithm was used to find the parameters of the LSSVM to obtain the fault diagnosis model combining Isomap and IChOA‐LSSVM. The model is compared with PSO‐LSSVM, ChOA‐LSSVM and GWO‐LSSVM. The diagnostic accuracy is 90.83%, 81.67%, 83.33% and 80%, respectively. The results demonstrate that the proposed method can effectively improve the performance of transformer fault diagnosis.

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“…According to whether the properties retained in manifold learning are global or local, manifold learning can be divided into global property preserving and local property preserving methods. As a classical manifold learning method with global property preservation, ISOMAP [10] calculated geodesic distance between all sample points and constructed global geodesic matrix to maintain global geometric properties of low-dimensional manifolds nested in high-dimensional space. However, in the face of large sample data, the calculation cost of ISOMAP algorithm is very high, resulting in low calculation efficiency.…”

Section: Related Workmentioning

confidence: 99%

Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

Hu,

Wang

2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction

Cited by 28 publications

References 26 publications

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

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