Twin Support Vector Machine for Clustering Wang, Zhen; Shao, Yuan-Hai; Bai, Lan ...
IEEE transaction on neural networks and learning systems,
10/2015, Letnik:
26, Številka:
10
Journal Article
The twin support vector machine (TWSVM) is one of the powerful classification methods. In this brief, a TWSVM-type clustering method, called twin support vector clustering (TWSVC), is proposed. Our ...TWSVC includes both linear and nonlinear versions. It determines k cluster center planes by solving a series of quadratic programming problems. To make TWSVC more efficient and stable, an initialization algorithm based on the nearest neighbor graph is also suggested. The experimental results on several benchmark data sets have shown a comparable performance of our TWSVC.
Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of ...nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM's mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called "iNitro-Tyr" was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.
Post-translational modifications (PTMs) play crucial roles in various cell functions and biological processes. Protein hydroxylation is one type of PTM that usually occurs at the sites of proline and ...lysine. Given an uncharacterized protein sequence, which site of its Pro (or Lys) can be hydroxylated and which site cannot? This is a challenging problem, not only for in-depth understanding of the hydroxylation mechanism, but also for drug development, because protein hydroxylation is closely relevant to major diseases, such as stomach and lung cancers. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods to address this problem. In view of this, a new predictor called "iHyd-PseAAC" (identify hydroxylation by pseudo amino acid composition) was proposed by incorporating the dipeptide position-specific propensity into the general form of pseudo amino acid composition. It was demonstrated by rigorous cross-validation tests on stringent benchmark datasets that the new predictor is quite promising and may become a useful high throughput tool in this area. A user-friendly web-server for iHyd-PseAAC is accessible at http://app.aporc.org/iHyd-PseAAC/. Furthermore, for the convenience of the majority of experimental scientists, a step-by-step guide on how to use the web-server is given. Users can easily obtain their desired results by following these steps without the need of understanding the complicated mathematical equations presented in this paper just for its integrity.
In this paper, we propose a nonparallel hyperplane support vector machine (NHSVM) for binary classification problems. Our proposed NHSVM is formulated by clustering the training points according to ...the similarity between classes. It constructs two nonparallel hyperplanes simultaneously by solving a single quadratic programming problem, and is consistent between its predicting and training processes - an essential difference that distinguishes it from other nonparallel SVMs. This proposed NHSVM has been analyzed theoretically and implemented experimentally. The results of experiments conducted using it on both artificial and publicly available benchmark datasets confirm its feasibility and efficacy, especially for “Cross Planes” datasets and datasets with heteroscedastic noise.
Multi-label learning paradigm, which aims at dealing with data associated with potential multiple labels, has attracted a great deal of attention in machine intelligent community. In this paper, we ...propose a novel multi-label twin support vector machine (MLTSVM) for multi-label classification. MLTSVM determines multiple nonparallel hyperplanes to capture the multi-label information embedded in data, which is a useful promotion of twin support vector machine (TWSVM) for multi-label classification. To speed up the training procedure, an efficient successive overrelaxation (SOR) algorithm is developed for solving the involved quadratic programming problems (QPPs) in MLTSVM. Extensive experimental results on both synthetic and real-world multi-label datasets confirm the feasibility and effectiveness of the proposed MLTSVM.
•The first nonparallel hyperplane SVM (MLTSVM) classifier applied in multi-label learning is proposed.•The multi-label information of the dataset can be effectively captured by multiple nonparallel hyperplanes.•The ambiguity of the predicting procedure is avoided by the effective decision function.•An efficient SOR algorithm is applied to solve the proposed MLTSVM.•Experimental results confirm the feasibility and superiority of the proposed MLTSVM.
In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with ...L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA.
As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation ...of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at http://app.aporc.org/iSNO-AAPair/, by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.
In this paper we formulate a least squares version of the recently proposed projection twin support vector machine (PTSVM) for binary classification. This formulation leads to extremely simple and ...fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers. Different from PTSVM, we add a regularization term, ensuring the optimization problems in our LSPTSVM are positive definite and resulting better generalization ability. Instead of usually solving two dual problems, we solve two modified primal problems by solving two systems of linear equations whereas PTSVM need to solve two quadratic programming problems along with two systems of linear equations. Our experiments on publicly available datasets indicate that our LSPTSVM has comparable classification accuracy to that of PTSVM but with remarkably less computational time.
► We propose a least squares projection twin support vector machine (LSPTSVM). ► A regularization term is added in our LSPTSVM. ► The optimization problems of LSPTSVM are positive definite and resulted better generalization ability. ► We just solve two systems of linear equations for LSPTSVM. ► LSPTSVM can easily handle large datasets.
Within the large scale classification problem, the stochastic gradient descent method called PEGASOS has been successfully applied to support vector machines (SVMs). In this paper, we propose a ...stochastic gradient twin support vector machine (SGTSVM) based on the twin support vector machine (TWSVM). Compared to PEGASOS, our method is insensitive to stochastic sampling. Furthermore, we prove the convergence of SGTSVM and the approximation between TWSVM and SGTSVM under uniform sampling, whereas PEGASOS is almost surely convergent and only has an opportunity to obtain an approximation to SVM. In addition, we extend SGTSVM to nonlinear classification problems via a kernel trick. Experiments on artificial and publicly available datasets show that our method has stable performance and can handle large scale problems easily.
In this paper, we propose an efficient weighted Lagrangian twin support vector machine (WLTSVM) for the imbalanced data classification based on using different training points for constructing the ...two proximal hyperplanes. The main contributions of our WLTSVM are: (1) a graph based under-sampling strategy is introduced to keep the proximity information, which is robustness to outliers, (2) the weight biases are embedded in the Lagrangian TWSVM formulations, which overcomes the bias phenomenon in the original TWSVM for the imbalanced data classification, (3) the convergence of the training procedure of Lagrangian functions is proven and (4) it is tested and compared with some other TWSVMs on synthetic and real datasets to show its feasibility and efficiency for the imbalanced data classification.
•We propose an efficient WLTSVM for imbalanced data classification.•A graph based under-sampling strategy is introduced, which is robustness to outliers.•The weight biases are embedded in our WLTSVM formulations.•One Lagrangian training algorithm is presented and its convergence is proven.•Experimental results show its feasibility and efficiency.
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