One class proximal support vector machines

Dufrenois, Franck; Noyer, Jean-Charles

doi:10.1016/j.patcog.2015.09.036

Cited by 20 publications

(20 citation statements)

References 17 publications

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“…For the intention to compare the performance of our NPTSVM with PTSVM and RPTSVM, we conduct experiments on lots of standard datasets used in [13], [14], [15]. All of the classification methods are implemented on a computer with Matlab 7.0.…”

Section: Resultsmentioning

confidence: 99%

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An Updated Projection Twin Support Vector Machine for Classification

Hua

2017

MATEC Web Conf.

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Abstract. Based on projection twin support vector machine (PTSVM) and its extensions, this paper describes an updated PTSVM (UPTSVM) for classification. Compared with existing PTSVMs, UPTSVM has its own advantages. First, similar to the standard support vector machine (SVM), UPTSVM maintains the consistency of the optimization problems in the linear and nonlinear case, which results in the nonlinear formulations can be directly turned into the linear ones. Nevertheless, the existing PTSVMs lose the consistency because of using empirical kernel to construct nonlinear formulations. Second, UPTSVM avoids the inverse of kernel matrixes in the course of solving dual problems, which indicates it can not only reduce computing time but also save storage space. Third, UPTSVM can be practically proved equivalent to the PTSVM with regularization (RPTSVM). Experimental results on lots of data sets show the virtue of the presented method.

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Section: Resultsmentioning

confidence: 99%

“…Compared with the dual problems (3) and (4) in RPTSVM, the dual problems (14) and (22) in UPTSVM avoid the inverse of kernel matrixes in the course of training, which can not only reduce computing time but also save storage space. The nonlinear UPTSVM has the same advantage.…”

Section: Comparision Of Uptsvm With Rptsvmmentioning

confidence: 99%

An Updated Projection Twin Support Vector Machine for Classification

Hua

2017

MATEC Web Conf.

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“…The model was then transformed to

\begin{matrix} left \\ left \min_{boldnormal w, ξ, boldnormal b} \frac{1}{2} {‖ w ‖}^{2} + ε true \sum_{n = 1}^{S} ξ_{n} \\ left s . t . {\begin{matrix} y_{n} (boldnormal w x_{n} - boldnormal b) \geq 1 - ξ_{n} \\ ξ_{n} \geq 0 \end{matrix}, n = 1, ..., S \end{matrix}

where ξ n denotes positive slack variables and ε denotes the error penalty. In the future, some advanced classifiers will be tested, such as nonparallel SVM, fuzzy SVM (Yang, 2015a), kernel SVM (Wu, 2012b), SVM decision tree (Dong, 2014), proximal SVM (Dufrenois and Noyer, 2016), twin SVM (Wang et al, 2016), etc.…”

Section: Methodsmentioning

confidence: 99%

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Wang

Yang

et al. 2016

Front. Comput. Neurosci.

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Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way.Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC).Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM).Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier.Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

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“…In [31], an incremental version of the method in [27] is proposed to increase computational efficiency. A generalised Rayleigh quotient specifically designed for outlier detection is presented in [28], [36] where the method tries to find an optimal hyperplane which is closest to the target data and farthest from the outliers utilising two scatter matrices corresponding to the outliers and target data. In [36], the generalised eigenvalue problem is replaced by an approximate conjugate gradient solution to moderate the computational cost of the method in [28].…”

Section: Related Workmentioning

confidence: 99%

Robust One-Class Kernel Spectral Regression

Arashloo

Kittler

2021

IEEE Trans. Neural Netw. Learning Syst.

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The kernel null-space technique and its regression-based formulation (called one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an effective and computationally attractive one-class classification framework. Despite its outstanding performance, the applicability of kernel null-space method is limited due to its susceptibility to possible training data corruptions and inability to rank training observations according to their conformity with the model. This work addresses these shortcomings by studying the effect of regularising the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation (OC-KSR). In this respect, first, the effect of a Tikhonov regularisation in the Hilbert space is analysed where the one-class learning problem in presence of contaminations in the training set is posed as a sensitivity analysis problem. Next, driven by the success of the sparse representation methodology, the effect of a sparsity regularisation on the solution is studied. For both alternative regularisation schemes, iterative algorithms are proposed which recursively update label confidences and rank training observations based on their fit with the model. Through extensive experiments conducted on different data sets, the proposed methodology is found to enhance robustness against contamination in the training set as compared with the baseline kernel null-space technique as well as other existing approaches in a one-class classification paradigm while providing the functionality to rank training samples effectively.

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One class proximal support vector machines

Cited by 20 publications

References 17 publications

An Updated Projection Twin Support Vector Machine for Classification

An Updated Projection Twin Support Vector Machine for Classification

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Robust One-Class Kernel Spectral Regression

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