Concept factorization (CF) has shown its great advantage for both clustering and data representation and is particularly useful for image representation. Compared with nonnegative matrix factorization (NMF), CF can be applied to data containing negative values. However, the performance of CF method and its extensions will degenerate a lot due to the negative effects of outliers, and CF is an unsupervised method that cannot incorporate label information. In this article, we propose a novel CF method, with a novel model built based on the maximum correntropy criterion (MCC). In order to capture the local geometry information of data, our method integrates the robust adaptive embedding and CF into a unified framework. The label information is utilized in the adaptive learning process. Furthermore, an iterative strategy based on the accelerated block coordinate update is proposed. The convergence property of the proposed method is analyzed to ensure that the algorithm converges to a reliable solution. The experimental results on four real-world image data sets show that the new method can almost always filter out the negative effects of the outliers and outperform several state-of-the-art image representation methods.

Emotion recognition leveraging electroencephalogram (EEG) signals has emerged as a pivotal area in affective computing. However, existing approaches often overlook the interaction between EEG and other modalities. This study introduces the Bimodal Emotion Recognition Network (BERN), an innovative framework designed to improve emotion recognition accuracy by integrating EEG and eye movement features. The BERN model employs a deep learning architecture tailored for EEG and eye movement data, utilizing 3D convolution for EEG feature extraction, alongside a refined residual connection structure for eye movement feature extraction. Subsequently, the model incorporates a cross-modal attention mechanism and feature fusion techniques to effectively integrate EEG and eye movement information, significantly enhancing emotional state recognition. Experimental results on the SEED-IV emotion dataset demonstrate that the fusion model achieves an impressive average accuracy of 83.33% across four classes, surpassing several outstanding unimodal and multimodal methods. These findings emphasize the effectiveness of combining EEG and eye movement features, enriching the information for emotion recognition and markedly enhancing the model’s overall recognition accuracy.

This article proposes a novel low-rank matrix factorization model for semisupervised image clustering. In order to alleviate the negative effect of outliers, the maximum correntropy criterion (MCC) is incorporated as a metric to build the model. To utilize the label information to improve the clustering results, a constraint graph learning framework is proposed to adaptively learn the local structure of the data by considering the label information. Furthermore, an iterative algorithm based on Fenchel conjugate (FC) and block coordinate update (BCU) is proposed to solve the model. The convergence properties of the proposed algorithm are analyzed, which shows that the algorithm exhibits both objective sequential convergence and iterate sequential convergence. Experiments are conducted on six real-world image datasets, and the proposed algorithm is compared with eight state-of-the-art methods. The results show that the proposed method can achieve better performance in most situations in terms of clustering accuracy and mutual information.