Twin-parametric margin support vector machine with truncated pinball loss

Wang, Huiru; Xu, Yitian; Zhou, Zhijian

doi:10.1007/s00521-020-05225-7

Cited by 36 publications

(10 citation statements)

References 23 publications

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“…To address the differentiable point cloud sampling, Lang et al [45] suggested using soft projection, followed by the construction of a framework for learnable sampling. There are also several models, such as PIE-Net [46], SK-Net [47], and KCNet [48], that can efficiently learn the feature information of point clouds and maintain points with significant features. Although the deep learning-based point cloud technique is typically quite effective, it requires data with defined feature points for training, but these data are typically difficult to obtain.…”

Section: Point Cloud Subsamplingmentioning

confidence: 99%

Fast subsampling strategy for point cloud based on novel octree coding

Zheng,

Wang,

Zha

et al. 2024

Meas. Sci. Technol.

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Owing to the continuous expansion in data scale, the calculation, storage, and transmission of 3D data have been plagued by numerous issues. The point cloud data, in particular, often contain duplicated and anomalous points, which can hinder tasks such as measurement. To address this issue, it is crucial to utilize point cloud preprocessing methods that combine subsampling and denoising. These methods help obtain clean, evenly distributed, and compact points to enhance the accuracy of the data. In this study, an efficient point cloud subsampling method is proposed that combines point cloud denoising capabilities. This method can effectively preserve salient features while improving the quality of point cloud data. By constructing the octree structure of the point cloud, the corresponding node code is obtained according to the spatial coordinates of the point cloud, and the feature vector of the node is calculated based on the analysis of covariance. Node feature similarity is introduced to distinguish the node into feature and non-feature nodes, forming the node feature code, and the layer threshold is introduced to filter outliers. Experimental results showed that this approach is effective in removing noise while preserving important features, thereby reducing the complexity of the point cloud. The algorithm can serve as a reference for fast and precise measurement that demands high timeliness owing to its high computational efficiency.

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Section: Point Cloud Subsamplingmentioning

confidence: 99%

Fast subsampling strategy for point cloud based on novel octree coding

Zheng,

Wang,

Zha

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…(2) Different from the monogeneity of feature point information in literature, [33][34][35][36][37] the algorithm determines feature contour points [50][51][52]…”

Section: Contributionmentioning

confidence: 99%

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Zhao,

Zhang,

et al. 2023

Measurement and Control

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For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.

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“…2.4 Twin support vector machine with truncated pinball loss TPin-TSVM [25] inherits the fast computational speed of TSVM maintaining sparsity and noise insensitivity. It uses a truncated pinball loss function which treating all samples equally.…”

Section: Twin Bounded Support Vector Machine With Pinball Lossmentioning

confidence: 99%

“…Scholars have conducted a lot of research on loss functions [22,24]. A twin-parametric margin support vector machine with truncated pinball loss (TPin-TSVM) [25] which applies the twin-parametric margin support vector machine (TPMSVM) with the truncated pinball loss to improve the robustness of Pin-TSVM, so it is insensitive to noise and outliers. A comparison of related models will be given in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Twin Bounded Support Vector Machine with Capped Pinball Loss

Wang,

Hong,

Zhang

2023

Preprint

Self Cite

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In this paper, we propose a novel classifier termed as twin bounded support vector machine with capped pinball loss (CPin-TBSVM), which has the excellent properties of being insensitive to noise. Given that the proposed model is non-convex, we use the convex-concave procedure algorithm (CCCP) to solve a series of two smaller-sized quadratic programming problems to find the optimal solution. In the process of solving the iterative sub-problem, the dual coordinate descent method (DCDM) is used for speeding up solving optimization problems. Moreover, we analyze its theoretical properties, including that the capped pinball loss satisfies Bayes’ rule and CPin-TBSVM has certain noise insensitivity and sparsity. The properties are verified on an artificial dataset as well. The numerical experiment is conducted on 24 UCI datasets and the results are compared with four other models which include SVM, TSVM, Pin-GTSVM and TPin-TSVM. The results show that the proposed CPin-TBSVM has a better classification effect and noise insensitivity.

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Twin-parametric margin support vector machine with truncated pinball loss

Cited by 36 publications

References 23 publications

Fast subsampling strategy for point cloud based on novel octree coding

Fast subsampling strategy for point cloud based on novel octree coding

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Twin Bounded Support Vector Machine with Capped Pinball Loss

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