Twin support vector machines with privileged information

Che, Zhiyong; Liu, Bo; Xiao, Yanshan; Cai, Hao

doi:10.1016/j.ins.2021.05.069

Cited by 11 publications

(2 citation statements)

References 41 publications

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“…• Supervised: AdaBoost with PI (Chen et al [21], Liu et al [22]), SVM+ for risk modelling (Ribeiro et al [23], [24]), SVM+ and multi-task learning (Liang and Cherkassky [25], Liang and Cherkassky [26], Liang et al [27], Cai and Cherkassky [28], Tang et al [29]), Regression Forests for facial feature detection with privileged head pose or gender (Yang and Patras [30]), image classification using privileged attributes, bounding box annotations, and textual descripting tags (Sharmanska et al [31], Sharmanska et al [32], Li et al [33], Wang and Ji [34], Yan et al [35], Rodríguez et al [36]), structured SVM (SSVM) prediction algorithm for image object localization using PI (Feyereisl et al [37]), unifying distillation and PI (Lopez-Paz et al [38]), multi-instance learning for action and event recognition with privileged web data (Niu et al [39]), knowledge transfer for neural networks (Vapnik and Izmailov [8]), image object detection using PI (Hoffman et al [40]), domain adaptation (Sarafianos et al [41]), multiview privileged SVMs (Tang et al [42]), deep learning under PI (Lambert et al [43]), PI for structured output prediction (Zhang et al [44]), label enhancement with multi-label learning (Zhu et al [45]), PI for the diagnosis of Alzheimer's disease (Li et al [46], Ganaie and Tanveer [47]), breast (Shaikh et al [48]) and liver (Zhang et al [49]) cancers, PI for image super-resolution using CNNs (Lee et al [50]), robust SVM+ (Li et al [9], Wu et al [51]), twin SVM with PI (Che et al [52]), robust twin SVM+ (Li et al [53]), Support Vector ...…”

Section: Related Work a Privileged Informationmentioning

confidence: 99%

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

Lange,

Smolyakov,

Burnaev

2023

IEEE Access

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One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data -Learning Using Privileged Information paradigm (LUPI, Vapnik and Vashist [1]). Sequential Minimal Optimization (SMO) method has been already developed for supervised Support Vector Machines (SVM) in Platt [2] and Keerthi et al. [3], for unsupervised (one-class) SVM in Schölkopf et al. [4], and for SVM with privileged information (SVM+) in Pechyony and Vapnik [5]. As can be seen, the missing brick in this research has long been a one-class SVM with privileged information (OC-SVM+). In this paper, we propose SMO algorithm for OC-SVM+ that significantly outperforms non-sequential algorithms for training the OC-SVM+ model. Its finite-time convergence is established. The experiments show how privileged information affects a descriptive domain in the space of original features. Comparative benchmark tests demonstrate that our algorithm is superior over interior point algorithms.

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Section: Related Work a Privileged Informationmentioning

confidence: 99%

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

Lange,

Smolyakov,

Burnaev

2023

IEEE Access

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“…The training data can be grouped according to a feature attribute, and a formal optimization formula is sorted out. Che et al (2021) proposed a twin support vector machine model for privileged information learning based on LUPI paradigm . Some other models have been improved in the SVM with privileged information to make the model more robust (Li et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Multi-view support vector machines with sub-view learning

et al. 2023

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Multi-view learning improves the performance of existing learning tasks by using complementary information between multiple feature sets. In the latest research, multi-view learning model using privilege information is proposed, specific models such as PSVM-2V and MCPK. In these models, views complement each other by acting as privileged information policies, However, a single view contains privilege information that can guide the classifier, and the existing framework does not consider it. In order to use this information to correct multi-view support vector machine classifier, we propose a framework for generating a series of small-scale views based on information hidden in a single view, which extends the original multi-view parallel structure to a hierarchical structure with sub-view mechanism. In this paper, two sub-view learning structures SL-PSVM-2V and SL-MCPK are constructed. The two new models fully exploit the data features in the view. Similarly, they follow the principles of consistency and complementarity. We use the standard quadratic programming solver to solve the new model. In 55 groups of classification experiments and noise sensitivity tests, the new model has better performance than the benchmark model. Statistical comparison shows that the new method is significantly different from the existing methods.

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An improved MLTSVM using label-specific features with missing labels

Li²,

Li³

et al. 2022

Appl Intell

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Twin support vector machines with privileged information

Cited by 11 publications

References 41 publications

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

Multi-view support vector machines with sub-view learning

An improved MLTSVM using label-specific features with missing labels

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