A collaborative-competitive representation based classifier model

Yuan, Haoliang; Li, Xuecong; Xu, Fangyuan; Wang, Yifei; Lai, Loi Lei; Tang, Yuan Yan

doi:10.1016/j.neucom.2017.09.022

Cited by 40 publications

(31 citation statements)

References 20 publications

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“…In this section, the extensive experiments on several face databases and some real numerical UCI data sets are conducted. In the experiments, we compare the proposed DCCRC with the state-of-the-art RBC methods including SRC [1], CRC [2], CCRC [46], Co-CRC [47], DSRC [44], ProCRC [22], and EProCRC [43]. It should be noted that all regularized parameters in the competing methods are preset as the range [10 − 3 , 10 − 2 , .…”

Section: Methodsmentioning

confidence: 99%

“…In many latest extensions of CRC, the class discrimination information of data in fact was fully employed for strengthening the power of the pattern classification [42][43][44][45][46][47]. From the point of view of probability, a probabilistic CRC (ProCRC) was developed by using the discriminative regularization of the representations between all the classes and each class [22].…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of DSRC and ProCRC, a novel discriminative CRC method was proposed to extend DSRC [45]. To overcome the issue that the phases of representation and classification in the most CRC variations are not integrated into a unified model, a collaborative and competitive representation-based classifier (CCRC) was proposed in [46]. CCRC directly includes the classification decision in its model and can enhance the training sample from each class to competitively represent each testing sample.…”

Section: Introductionmentioning

confidence: 99%

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Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

et al. 2019

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Recently, collaborative representation-based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation-based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative l2-norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l2-norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state-of-the-art representation-based classification methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

et al. 2019

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“…Table 1 shows the classification accuracy using different methods in the Extended Yale B database. As in [23,29] database as the training set and the rest as the test set. To ensure the objectivity of the experimental data, all data are not processed for dimensionality reduction.…”

Section: ) Parameter Settingmentioning

confidence: 99%

“…Table 2 shows the classification accuracy of different methods used in the Georgia Tech database. As in [23,29], We randomly selected 1, 2, 3, 4, 5 and 6 faces from each class of the Georgia Tech database as the training set and the rest as the test set. Table 2 shows that methods based on robust regression, such as SRC, LRC, and CRC, have poor performance in handling light changes.…”

Section: ) Parameter Settingmentioning

confidence: 99%

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Cheng

2020

IEEE Access

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The representation-based classification method has become a research hotspot in recent years. Representation-based classifiers assign class labels directly to test samples based on a structured dictionary. The structured dictionary is composed of training samples. We call the samples in the dictionary as atoms. To further improve the expression ability of different training atoms in the classifier to the test samples, we propose an ensemble-enhanced sparse classification algorithm based on the correlation coefficient. The model proceeds from two aspects: the representation coefficient level and the similarity between different samples. First, a sparse dictionary is combined with the multiplicative property of training samples to build and solve the sparse and collaborative representation algorithm. The fusion representation coefficients are obtained by weighted sparse representation coefficients and collaborative representation coefficients. The test samples are reconstructed by fusing the representation coefficients, and the minimization recovery residuals for each class of samples are calculated. Second, the correlation coefficient value between the test sample and the training sample is calculated, and the maximum correlation quotient between the test sample and the training sample is obtained. Finally, the maximum correlation quotient and the minimization recovery residual are weighted, and the decision classification is carried out in the decision function to achieve the final face recognition. Experiments on AR, Extended Yale B, Georgia Tech and other general face databases show the effectiveness of the algorithm. The main contribution of this model is that it is more robust than a single sparse or collaborative representation model and can improve recognition accuracy.

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Class mean‐weighted discriminative collaborative representation for classification

Gou

Song

et al. 2021

Int J Intell Syst

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Representation-based classification (RBC) has been attracting a great deal of attention in pattern recognition. As a typical extension to RBC, collaborative representation-based classification (CRC) has demonstrated its superior performance in various image classification tasks. Ideally, we expect that the learned class-specific representations for a testing sample are discriminative, and the representation computed for the true class dominates the final representation of the testing sample. Most existing CRC-based methods can learn pattern discrimination, but cannot differentiate the contribution of class-specific representations to the classification of each testing sample. It is challenging for a representation-based classifier to retain both properties. To address this challenge and further improve CRC's classification performance, we propose a novel CRC-based method, class mean-weighted discriminative collaborative representation-based classifier (CMW-DCRC). Its objective function penalises the standard l 2 -norm residuals with two discriminative regularisation terms. A decorrelating term makes the class-specific representations more discriminative, and a newly designed class meanweighted term that promotes the training samples from individual classes to competitively reconstruct the testing sample while boosting the contribution of the true class. To further enhance the robustness of CRC, we extend CMW-DCRC by replacing the l 2 -norm coding residual with a l 1 -norm coding residual, and solve the optimisation problem with an iteratively reweighted least square algorithm. Extensive experimental results on nine image data sets have shown that our methods outperform the state-of-the-art RBC-based methods.

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A collaborative-competitive representation based classifier model

Cited by 40 publications

References 20 publications

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Class mean‐weighted discriminative collaborative representation for classification

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A collaborative-competitive representation based classifier model

Cited by 40 publications

References 20 publications

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l2‐Norm Regularizations

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l2‐Norm Regularizations

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Class mean‐weighted discriminative collaborative representation for classification

Contact Info

Product

Resources

About

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations