Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis

Ye, Qiaolin; Fu, Liyong; Zhang, Zhao; Zhao, Henghao; Naiem, Meem

doi:10.1016/j.neunet.2018.05.020

Cited by 49 publications

(22 citation statements)

References 41 publications

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“…However, the related Lp-norm researches still employ non-robust squared L2-norm in the objectives. Previous our work [57] has shown that Lp-norm distance metric is a better choice for improving robustness, who redesigned a flexible linear discriminant analysis via simultaneous Ls-norm distance maximization and Lp-norm distance minimization (FLDA-Lsp), which solves a new more effective iterative algorithm for the solution to the objective as an excellent contribution, and the result of this research shows the convergence and superiority of Lp-norm distance metric.…”

Section: Introductionmentioning

confidence: 74%

“…Of course, the differences between the proposed method and our previous work, in addition to the multi-view learning, there is a dimension reduction algorithm in [57] but ours is a classification method. In specific, the main contributions of this paper is shown as follows:…”

Section: Introductionmentioning

confidence: 93%

“…Under the inspiration of the works of multi-view learning [25], [33], [37] and robust Lp-norm distance measure [57], we propose a robust multi-view GEPSVM algorithm, Lp,s-MvGEPSVM, which is based on the Lp-norm distance minimization and Ls-norm maximization.…”

Section: Introductionmentioning

confidence: 99%

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Multi-View Learning With Robust Generalized Eigenvalue Proximal SVM

Huang

Yang

et al. 2019

IEEE Access

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Multi-view learning mechanism, which enhances learning performance by training multi-model data sets, is a popular filed in recent years. Multi-view generalized eigenvalue proximal support vector machine (MvGSVM), as a most recently proposed classifier, has been shown to be successful in multi-model classification, which incorporates multi-view learning into classical GEPSVM. However, this method is still based on squared L2-norm distance measure, thus its robustness is not guaranteed in the presence of outliers. To address this problem, we propose a robust multi-view GEPSVM based on Lp-norm minimization and Ls-norm maximization. But, the introduction of the Lp-norm and Ls-norm makes the problem different from the generalized eigenvalue problem. So an efficient iterative algorithm is designed to solve this problem, and we also give the proof of convergence of the algorithm. The performances in extensive experiments demonstrate the effectiveness and robustness of the algorithm.INDEX TERMS Robustness, multi-view learning, classification, generalized eigenvalue proximal support vector machine (GEPSVM).

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 93%

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Multi-View Learning With Robust Generalized Eigenvalue Proximal SVM

Huang

Yang

et al. 2019

IEEE Access

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“…Norm [ 29 ] is a reinforced notion of distance, which by definition adds a scalar multiplication algorithm to distance. Sometimes we can think of the norm as a distance for the sake of understanding.…”

Section: Related Workmentioning

confidence: 99%

Group-based local adaptive deep multiple kernel learning with lp norm

Ren¹,

Fa²,

Zhou³

et al. 2020

PLoS ONE

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The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning. However, the existing DMKL methods are hard to find suitable global model parameters to improve classification accuracy in numerous datasets and do not take into account inter-class correlation and intra-class diversity. In this paper, we present a group-based local adaptive deep multiple kernel learning (GLDMKL) method with lp norm. Our GLDMKL method can divide samples into multiple groups according to the multiple kernel k-means clustering algorithm. The learning process in each well-grouped local space is exactly adaptive deep multiple kernel learning. And our structure is adaptive, so there is no fixed number of layers. The learning model in each group is trained independently, so the number of layers of the learning model maybe different. In each local space, adapting the model by optimizing the SVM model parameter α and the local kernel weight β in turn and changing the proportion of the base kernel of the combined kernel in each layer by the local kernel weight, and the local kernel weight is constrained by the lp norm to avoid the sparsity of basic kernel. The hyperparameters of the kernel are optimized by the grid search method. Experiments on UCI and Caltech 256 datasets demonstrate that the proposed method is more accurate in classification accuracy than other deep multiple kernel learning methods, especially for datasets with relatively complex data.

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“…The image quality measure (IQM) includes pixel difference measures, correlation-based measures, edge-based measures, etc. Once the feature vector is generated, the input image is classified as real or fake using a simple Linear Discriminant Analysis (LDA) classifier [51]. However, the IQM approach largely depends on the quality of the spoof video and hence does not yield good results on various data in CASIA-FASD.…”

Section: Related Workmentioning

confidence: 99%

Face Spoofing Attack Detection Using Spatial Frequency and Gradient-Based Descriptor

Ali¹,

Park²

2019

KSII TIIS

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Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

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Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis

Cited by 49 publications

References 41 publications

Multi-View Learning With Robust Generalized Eigenvalue Proximal SVM

Multi-View Learning With Robust Generalized Eigenvalue Proximal SVM

Group-based local adaptive deep multiple kernel learning with lp norm

Face Spoofing Attack Detection Using Spatial Frequency and Gradient-Based Descriptor

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