A class-specific mean vector-based weighted competitive and collaborative representation method for classification

Gou, Jianping; He, Xiyun; Jun-yu, LU; Ma, Hongwen; Ou, Weihua; Yuan, Yunhao

doi:10.1016/j.neunet.2022.02.021

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…In addition, some optimization algorithms [18,19] have recently been proposed to obtain better classification results and semantic representations, and contrastive learning is one of them. Contrastive learning aims to learn effective representations by pulling semantically similar sentences together and pushing dissimilar sentences apart [20].…”

Section: Related Workmentioning

confidence: 99%

Keyword-Enhanced Multi-Expert Framework for Hate Speech Detection

Zhong

et al. 2022

Mathematics

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The proliferation of hate speech on the Internet is harmful to the psychological health of individuals and society. Thus, establishing and supporting the development of hate speech detection and deploying evasion techniques is a vital task. However, existing hate speech detection methods tend to ignore the sentiment features of target sentences and have difficulty identifying some implicit types of hate speech. The performance of hate speech detection can be significantly improved by gathering more sentiment features from various sources. In the use of external sentiment information, the key information of the sentences cannot be ignored. Thus, this paper proposes a keyword-enhanced multiexperts framework. To begin, the multi-expert module of multi-task learning is utilized to share parameters and thereby introduce sentiment information. In addition, the critical features of the sentences are highlighted by contrastive learning. This model focuses on both the key information of the sentence and the external sentiment information. The final experimental results on three public datasets demonstrate the effectiveness of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Keyword-Enhanced Multi-Expert Framework for Hate Speech Detection

Zhong

et al. 2022

Mathematics

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“…Afterwards, many improved methods were proposed to further boost the classification performance of CRC. Gou et al [32] developed a class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR) method, which fully employs the discrimination information in different ways. Motivated by the idea of linear representation, Gou et al [33] proposed a representation coefficient-based k-nearest centroid neighbor (RCKNCN) method.…”

Section: Collaborative-representation-based Classificationmentioning

confidence: 99%

Face Recognition via Compact Second-Order Image Gradient Orientations

Yin

et al. 2022

Mathematics

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Conventional subspace learning approaches based on image gradient orientations only employ first-order gradient information, which may ignore second-order or higher-order gradient information. Moreover, recent researches on the human vision system (HVS) have uncovered that the neural image is a landscape or a surface whose geometric properties can be captured through second-order gradient information. The second-order image gradient orientations (SOIGO) can mitigate the adverse effect of noise in face images. To reduce the redundancy of SOIGO, we propose compact SOIGO (CSOIGO) by applying linear complex principal component analysis (PCA) in SOIGO. To be more specific, the SOIGO of training data are firstly obtained. Then, linear complex PCA is applied to obtain features of reduced dimensionality. Combined with collaborative-representation-based classification (CRC) algorithm, the classification performance of CSOIGO is further enhanced. CSOIGO is evaluated under real-world disguise, synthesized occlusion, and mixed variations. Under the real disguise scenario, CSOIGO makes 2.67% and 1.09% improvement regarding accuracy when one and two neutral face images per subject are used as training samples, respectively. For the mixed variations, CSOIGO achieves a 0.86% improvement in terms of accuracy. These results indicate that the proposed method is superior to its competing approaches with few training samples, and even outperforms some prevailing deep-neural-network-based approaches.

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“…In order to improve the generalization capability, many methods impose constraints on representation coefficients in the field of face recognition, e.g., competitive collaborative representation classification (Co-CRC) [39], probabilistic collaborative representation classification (ProCRC) [40], and so on; the main purpose of all these methods is to make representation coefficients to be clustered in the correct class. Recently Gou et al [41] proposed a collaborative representation model based on mean vector and weighted competition (CMWCCR) for face recognition, which adds competition, mean vector, and weighting three items as constraints of the objective function to the CRC, and considers the similarity between testing samples and the dictionary in many aspects, thus showing better classification results. Considering that too many constraints may bring a large number of too many model hyperparameters, a new classification model can be constructed for the classification of hyperspectral images by creating suitable constraint terms.…”

Section: Introductionmentioning

confidence: 99%

Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint Classification for Hyperspectral Images

Su,

Shi,

Xue

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Collaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most collaborative representation classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and heterogeneity problems. Therefore, this paper proposes a mean-weighted collaborative representation classification model (MWCRC) based on the joint spatial-spectral data. It imposes mean and weighted constraints on the representation coefficients based on CR, which attenuates the noise effect and increases the distinguishability between classes. Second, a sample augmentation method based on the principle of minimizing the representation residuals is proposed. Sample augmentation is realized through initial classification and calculation of representation residuals to achieve the objective of consolidating model stability and improving classification accuracy. Meanwhile, in order to alleviate the problem of spatial homogeneity and heterogeneity, the extended morphological profile (EMP) and the stacking approach are utilized to construct the joint spatial-spectral data for the classification of MWCRC. The superiority of the proposed method is demonstrated by experimental validation using a small number of training samples in three real datasets.

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A class-specific mean vector-based weighted competitive and collaborative representation method for classification

Cited by 15 publications

References 45 publications

Keyword-Enhanced Multi-Expert Framework for Hate Speech Detection

Keyword-Enhanced Multi-Expert Framework for Hate Speech Detection

Face Recognition via Compact Second-Order Image Gradient Orientations

Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint Classification for Hyperspectral Images

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