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Collaborative representation-based classification (CRC), as a typical kind of linear representation-based classification, has attracted more attention due to the effective and efficient pattern classification performance. However, the existing class-specific representations are not competitively learned from collaborative representation for achieving more informative pattern discrimination among all the classes. With the purpose of enhancing the power of competitive and discriminant representations among all the classes for favorable classification, we propose a novel CRC method called the class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR). The CMWCCR mainly contains three discriminative constraints including the competitive, mean vector and weighted constraints that fully employ the discrimination information in different ways. In the competitive constraint, the representations from any one class and the other classes are adapted for learning competitive representations among all the classes. In the newly designed mean vector constraint, the mean vectors of all the class-specific training samples with the corresponding class-specific representations are taken into account to further enhance the competitive representations. In the devised weighted constraint, the class-specific weights are constrained on the representation coefficients to make the similar classes have more representation contributions to strengthening the discrimination among all the class-specific representations. Thus, these three constraints in the unified CMWCCR model can complement each other for competitively learning the discriminative class-specific representations. To verify the CMWCCR classification performance, the extensive experiments are conducted on twenty-eight data sets in comparisons with the state-of-the-art representation-based classification methods. The experimental results show that the proposed CMWCCR is an effective and robust CRC method with satisfactory performance. • Design mean vector constraint for representation-based classification. • Design weighted constraint on the representation coefficients. • Propose CMWCCR method via competitive representation. [ABSTRACT FROM AUTHOR]

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 l2‐norm residuals with two discriminative regularisation terms. A decorrelating term makes the class‐specific representations more discriminative, and a newly designed class mean‐weighted 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 l2‐norm coding residual with a l1‐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. [ABSTRACT FROM AUTHOR]

Image classification is a hot technique applied in many multimedia systems, where both l1 and l2 regularizations have shown potential for robust sparse representation-based image classification. However, previous studies showed that l1 or l2 alone cannot ensure a robust result. The robustness of a classifier depends on the nature of the dataset most of the time. What is worse, data augmentation may make the dataset more complicated, which leads a sparse model become harder to optimize. In this paper, a novel sparse representation that learns double weights through data augmentation is proposed for robust image classification. The first weight combines the two coefficients solved by l1 and l2 regularizations to obtain a more discriminative representation, while the second weight integrates the residuals obtained from the original and virtual samples, to take full advantage of diversity created by data augmentation. The double-weight process builds a robust model that is able to deal with the augmented but variational datasets. Experiments on popular facial and object datasets demonstrate the promising performance of the proposed method. Learning double weights via sample virtualization is helpful to develop multimedia applications. [ABSTRACT FROM AUTHOR]

Collaborative representation-based classification (CRC) is one of the famous representation-based classification methods in pattern recognition. However, a testing sample in most of the CRC variants is collaboratively reconstructed by a linear combination of all the training samples from all the classes, the training samples from the class that the testing sample belongs to have no advantage in discriminatively and competitively representing and classifying the testing sample. Moreover, the incorrect classification can easily come into being when the training samples from the different classes are very similar. To address the issues, we propose a novel discriminative collaborative representation-based classification (DCRC) method via l 2 regularizations to enhance the power of pattern discrimination. In the proposed model, we consider not only the discriminative decorrelations among all the classes, but also the similarities between the reconstructed representation of all the classes and the class-specific reconstructed representations in the l 2 regularizations. The experiments on several public face databases have demonstrated that the proposed DCRC effectively and robustly outperforms the state-of-the-art representation-based classification methods. [ABSTRACT FROM AUTHOR]

Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification performance. However, most of them ignore the inter-class pattern discrimination among the class-specific representations, which is very critical for strengthening the pattern discrimination of collaborative representation (CR). In this article, we propose a novel CR approach for image classification, called weighted discriminative collaborative competitive representation (WDCCR). The proposed WDCCR designs the discriminative and competitive collaborative representation among all the classes by fully considering the class information. On the one hand, we incorporate two discriminative constraints into the unified WDCCR model. Both constraints are the competitive class-specific representation residuals and the pairs of class-specific representations for each query sample. On the other hand, the constraint of the weighted categorical representation coefficients is introduced into the proposed model for further enhancing the power of discriminative and competitive representation. In the weighted constraint, we assume that the different classes of each query sample should have less contribution to the representation with the small representation coefficients, and then two types of weight factors are designed to constrain the representation coefficients. Furthermore, the robust WDCCR (R-WDCCR) is proposed with l 1 -norm representation fidelity for recognizing noisy images. Extensive experiments on six image data sets demonstrate the effective and robust superiorities of the proposed WDCCR and R-WDCCR over the related state-of-the-art representation-based classification methods. • Propose two WDCCR methods with l 2 -norm fidelity. • Propose two robust WDCCR methods with l 1 -norm fidelity. • Design two weight factors in the proposed WDCCR methods. [ABSTRACT FROM AUTHOR]

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 l 2 -norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l 2 -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. [ABSTRACT FROM AUTHOR]

• Introduce the coarse to fine probabilistic collaborative representation. • Propose two-phase PCRC method with l1-norm and l2-norm fidelities. • Propose two-phase weighted PCRC method with l1-norm and l2-norm fidelities. The probabilistic collaborative representation-based classification (PCRC), as a novel extension of collaborative representation-based classification (CRC), is a promising method in pattern recognition. In this article, we adopt the coarse to fine representation to propose two-phase probabilistic collaborative representation based-classification (TPCRC) to enhance the power of pattern discrimination in PCRC. In TPCRC, the first phase is to utilize probabilistic collaborative representation to coarsely choose the nearest representative samples, and the second phase is to use the chosen nearest samples to finely represent and classify each testing sample. In order to employ the locality of data to further improve classification performance of PCRC, we also propose two-phase weighted probabilistic collaborative representation based-classification (TWPCRC). In the fine representation of TWPCRC, the probabilistic collaborative coefficients are weighted by the local distance similarities between each testing sample and all the training samples. The proposed methods are verified by the comparative experiments on three public image data sets. Experimental results show that the proposed methods outperform the state-of-the-art collaborative representation-based classification methods. [ABSTRACT FROM AUTHOR]

Collaborative representation-based classification has shown promising results on cognitive vision tasks like face recognition. It solves a linear problem with l 1 or l 2 norm regularization to obtain a stable sparse representation. Previous studies showed that the collaboration representation assisted the output of optimum sparsity constraint, but the choice of regularization also played a crucial role in stable representation. In this paper, we proposed a novel discriminative collaborative representation-based classification method via regularization implemented by truncated total least squares algorithm. The key idea of the proposed method is combining two coefficients obtained by l 2 regularization and truncated TLS-based regularization. After evaluated by extensive experiments conducted on several benchmark facial databases, the proposed method is demonstrated to outperform the naive collaborative representation-based method, as well as some other state-of-the-art methods for face recognition. The regularization by truncation effectively and dramatically enhances sparsity constraint on coding coefficients in collaborative representation and increases robustness for face recognition. [ABSTRACT FROM AUTHOR]

Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in image classification. However, the representation fidelity is usually measured by the ℓ 2 -norm , which is not robust to outliers. Moreover, CRC and PCRC only consider the global distribution of data and ignore the locality of data. To overcome those problems, in this paper, we propose the residual-based extensions and the weighted version of CRC and PCRC. Specifically, for the residual-based extensions of CRC and PCRC, we measure the representation fidelity by jointing ℓ 1 -norm and the ℓ 2 -norm of coding residuals. For the weighted extensions of CRC and PCRC with different coding residuals, we constrained the representation coefficients with locality of data. To verify the effectiveness of the proposed methods, we conduct extensive experiments on six popular face databases and three image databases. Experimental results have demonstrated that the ℓ 1 -norm of coding residual, jointing both the ℓ 1 -norm and the ℓ 2 -norm of coding residuals and the locality constraint of representation coefficients can enhance pattern discrimination effectively. [ABSTRACT FROM AUTHOR]

Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet been fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l 2 -norm regularization of residuals between each query sample and the class-specific representations, the other one is the l 2 -norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. We study the effectiveness and robustness of the proposed method by comparing it with the state-of-the-art CRC methods on six face databases and twelve UCI data sets. The experimental results demonstrate the promising classification performance of the proposed method. [ABSTRACT FROM AUTHOR]

• Propose the WELRMC method. • Propose the TPWELRMC method. • Design weighted sparsity augmented collaborative representation. • Design coarse to fine augmented representation. Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods. [ABSTRACT FROM AUTHOR]

In multi-label classification, challenges arise from missing labels due to subjective analysis or label ambiguity. This makes it difficult to accurately capture label correlations and enhance classifier performance. Previous research has primarily focused on obtaining label correlations with sparsity assumptions from incomplete label matrices to fill in these missing labels. However, this approach leads to inaccurate label correlations since it relies on incomplete label matrix information. To overcome this issue and improve accuracy, this paper proposes a new method called LCFM, which effectively learns label correlations using a combination of global and local label-specific features to recover missing labels. Firstly, we rely on the sparsity assumption of Lasso regression coefficients to select label-specific features from the training samples. Secondly, we introduce l 2 -norm regularization to learn global label correlations through global label-specific features. simultaneously, we utilize similar label-specific features to describe local label correlations. Finally, we minimize the difference between predicted results and true labels using the binary cross-entropy loss function and employ the Accelerated Proximal Gradient (APG) algorithm for model optimization. Experimental results comparing LCFM with five benchmark indicators on nine datasets demonstrate that our proposed method outperforms other advanced methods, showing strong competitiveness. [ABSTRACT FROM AUTHOR]