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Guolin Yu

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5 papers

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EAAI Journal 2024 Journal Article

Fast sparse twin learning framework for large-scale pattern classification

Haoyu Wang
Guolin Yu
Jun Ma

Lately, twin support vector machine and its variants have received extensive attention and in-depth research in the field of large-scale pattern classification. However, they may lead to high computational cost, which greatly affects their development and research. Aiming to solve this problem, this paper a novel fast sparse twin learning framework for large-scale data classification is proposed. In this learning framework, by introducing sparse constraints into the dual problem, the number of support vectors can be effectively reduced in dual space, so as to improve the computational speed of the model. Importantly, the learning framework is not only sparsity for large-scale samples classification, but also insensitive to sample noise, thus it is stable for resampling. In addition, we use the modified Newton’s method to deal with the optimization problem with sparse constraints. Numerical experiments are carried out on ten large-scale datasets. The results show that the proposed learning framework for large-scale data classification problems has significant advantages and comparability with other learning methods in terms of computational speed and classification accuracy.

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EAAI Journal 2024 Journal Article

Sparse robust adaptive unsupervised subspace learning for dimensionality reduction

Weizhi Xiong
Guolin Yu
Jun Ma
Sheng Liu

This work is devoted to the investigation of dimension reduction problem. As an efficient dimension reduction method, much attention has been paid on unsupervised subspace learning since it does not rely on expensive labels. Firstly, we implant a robust estimator in the error term of objective function, this leads to that small coefficients can be automatically allocated to the abnormal points. Thus, our model is robust to noise and outliers. Posteriorly, the L 2, r -norm ( 1 ≤ r ≤ 2 ) is used as a measure of error, then, the performance of the model can be improved by selecting the appropriate adaptive parameter r. Further, a L 2, p -norm ( 0 < p ≤ 1 ) regularization term is added to the objective function, therefore the gained sparse subspace can further improve the efficiency and accuracy of the expression, as well as enhance the generalization ability of the model and reduce over-fitting. Moreover, an efficient algorithm with fast convergence speed is designed to solve the model. Finally, the experimental results on 14 datasets show that the subspace dimension obtained by our method is no more than 6. From the results of classification test, our algorithm has obvious advantages over the other similar six algorithms.

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EAAI Journal 2023 Journal Article

Hessian scatter regularized twin support vector machine for semi-supervised classification

Guolin Yu
Jun Ma
Chenzhen Xie

Currently, semi-supervised twin support vector machine based on Laplacian regularization (LapTSVM) have received extensive attention and research in many fields of machine learning. Unfortunately, Laplacian regularization has a constant null space, so the solution is often a constant function and cannot well maintain the local topology of the samples. Aiming the above urgent problems, this paper, we first construct a Hessian scatter regularization (HSR) term. HSR has two major advantages: (1) HSR prefers linear variation in function values along of the geodesic distance and maintains the local manifold structure of the samples well. (2) HSR tries to find the projection from the original space to the feature space to maximize the inter-class scatter and minimize the intra-class scatter of the samples; the scatter is regarded as the discriminative information (structural information) of samples. Secondly, by introducing HSR, we propose a Hessian scatter regularized twin support vector machine (HSR-TSVM). Compared with LapTSVM, HSR-TSVM uses the global and local structure information of the sample to overcome the shortcomings of insufficient extrapolation caused by Laplacian regularization, while retaining almost all the advantages of the classic LapTSVM. Furthermore, to improve the computational efficiency of HSR-TSVM, the least-squares version of HSR-TSVM, namely HSR-LSTSVM, is proposed, and the conjugate gradient method is used to solve it. Experimental results on four synthetic datasets, ten UCI datasets, and four image datasets show that the proposed methods are competitive with semi-supervised learning methods based on Laplacian regularization.

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EAAI Journal 2023 Journal Article

Safe semi-supervised learning for pattern classification

Jun Ma
Guolin Yu
Weizhi Xiong
Xiaolong Zhu

Semi-supervised learning (SSL) based on manifold regularization in many fields has attracted widespread attention and research. However, SSL still has two main challenges: On the one hand, studies have shown that unlabeled data may cause performance degradation in semi-supervised classifiers, which means that unlabeled data introduces uncertainty and potential hazards. On the other hand, for samples distributed on different class boundaries, manifold regularization is not necessarily satisfactory, which will result in samples near the boundary that are likely to be misclassified. In response to the above problems, we propose a new SSL framework called safe semi-supervised learning (Sa-SSLJR for short). In Sa-SSLJR, a risk degree regularization term is constructed to estimate the uncertainty and potential risk of unlabeled data in the semi-supervised learning process. Secondly, based on manifold regularization and discriminant regularization, a joint regularization term is developed to solve the second challenge of SSL. Extensive experiments on multiple datasets show that our approach is competitive with state-of-the-art methods in terms of classification performance and feasibility.

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EAAI Journal 2022 Journal Article

Regularized twin minimax probability machine for pattern classification and regression

Jun Ma
Guolin Yu

As an excellent discriminant classifier based on generating prior knowledge, the minimax probability machine (MPM) has been widely used and deeply researched in many fields. The core idea of minimax probability machine is to directly estimate probability accuracy bound by minimizing the maximum probability of misclassification. However, minimax probability machine does not include a regularization term for the construction of the separating hyperplane, and it needs to solve a large-scale second-order cone programming problem in the solution process, which greatly limits it development and application. In this paper, to improve the performance of minimax probability machine, we propose a novel binary classification method called regularized twin minimax probability machine classification (TMPMC). The TMPMC constructs two non-parallel hyperplanes for final classification by solving two smaller second-order cone programming problems to improve the performance of the MPM. For each hyperplane, our method is theoretically well grounded on the idea of minimizing the worst case (maximum) probability of misclassification of a class of samples while the distance to the other class is as large as possible. Our approach was first derived as linear methods, and subsequently extended as kernel-based strategies for nonlinear classification. Additionally, we extend TMPMC to the regression problem and propose a new regularized twin minimax probability machine regression (TMPMR). Experimental results on several datasets show that our methods are competitive in terms of generalization performance compared to other algorithms.

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