Weighted Neighborhood Preserving Ensemble Embedding

Mehta, Sumet; Zhan, Bisheng; Shen, Xiang-Jun

doi:10.3390/electronics8020219

Cited by 13 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main nonlinear methods are Kernel PCA, Laplacian Eigenmaps [2] Isomap [13], FastMap, Locally Linear Embedding [27]. However, these nonlinear methods are afflicted with the out-of-sample problem [28,29]. Hinton and Maaten proposed high-dimensional local and global structure methods, which are known as t-SNE [9].…”

Section: A Dimension Reductionmentioning

confidence: 99%

FastIsomapVis: A Novel Approach for Nonlinear Manifold Learning

et al. 2020

View full text Add to dashboard Cite

The classical Isomap is the most common unsupervised nonlinear manifold method and widely being used in visualizations and dimension reductions. However, when it applied to real-world datasets, it shows shortcomings for the shortest path between all pairs of data points, which are based on the nearest neighborhood G graph via the Dijkstra algorithm, which makes it a very time-consuming step. The other critical problem is the classical Isomap has a lack of topological stability on the nearest neighborhood G graph. In this paper, we propose a novel technique called the FastIsomapVis for the above problems of the classical Isomap. The FastIsomapVis uses hierarchal divide, conquer, and combine approach through two algorithms, which are randomized division tree (KD-tree) and Dijkstra Buckets Double (DKD). The primary aim of the FastIsomapVis is to increase the efficiency and accuracy of the graph. This research paper focuses on transforming the high dimensional datasets into a low dimensional Isomap visualization. The FastIsomapVis makes it easy to construct an accurate K nearest neighborhood G graph and scale high dimensional data points into low dimensional space. Our proposed method is compared to the classical Isomap to verify its effectiveness and provide highly authentic results of the high dimensional datasets. The finding of the current study shows that our proposed method is much fastened than classical Isomap.

show abstract

Section: A Dimension Reductionmentioning

confidence: 99%

FastIsomapVis: A Novel Approach for Nonlinear Manifold Learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…As the linear approximation of LLE, NPE algorithm can 1 College of Electrical and Information Engineering, Lanzhou University of Technology, China calculate an accurate linear projection matrix, which ensures real-time modeling of online data. Therefore, it has been successfully applied in the fields of face recognition (Mehta et al, 2019;Ran et al, 2018) and biomedicine (Zhang et al, 2015). Currently, NPE has also been introduced into fault detection (Hui and Zhao, 2020;Zhao and Wang, 2017).…”

Section: Introductionmentioning

confidence: 99%

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

Yao

Zhao

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

show abstract

“…To solve the above problems, an adaptive neighborhood selection method is introduced into the NPE algorithm, and a building extraction method of SAR, Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) is proposed. The ANSNPE algorithm is applied to polarimetric SAR feature extraction, the SVM algorithm is used to classify the extracted features, and different extraction algorithms are compared [27,47]. Section 2 introduces PolSAR image features.…”

Section: Introductionmentioning

confidence: 99%

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Cheng

Cui

et al. 2020

IJGI

View full text Add to dashboard Cite

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.unsupervised fuzzy mean classification method to extract the building area. Texture feature images and filter images obtained by wavelet transform, according to Zhu et al. [5], are used as an input layer of the BP (Back Propagation) neural network, and the texture classification of high-resolution SAR images is completed. However, in the field of radar remote sensing, a new synthetic aperture radar (SAR) satellite constellation has been successfully launched in recent years. As SAR data have shown explosive growth, the SAR big data era has been flourishing. High-resolution polarimetric synthetic aperture radar (referred to as PolSAR) images can obtain ground scene information from multiple dimensions, on the one hand to provide rich information for identification of features and on the other hand to increase the complexity of information extraction. In the context of big data, rapid and accurate automatic extraction technology is the future development trend and is of great significance in promoting the application of PolSAR.Feature extraction is the most critical and central part of information extraction on SAR images. Studying the feature extraction method for high-resolution PolSAR images, reducing the space dimension and removing redundant information are highly important for the fast and accurate automatic extraction of building areas. Traditional feature extraction methods based on the global linear structure hypothesis premise feature of a PolSAR image set are proposed. Although they can achieve the purpose of reducing dimension, the intrinsic structure of high-dimension...

show abstract

Weighted Neighborhood Preserving Ensemble Embedding

Cited by 13 publications

References 35 publications

FastIsomapVis: A Novel Approach for Nonlinear Manifold Learning

FastIsomapVis: A Novel Approach for Nonlinear Manifold Learning

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Contact Info

Product

Resources

About