Publications

In order to process multiview multilabel, multilabel, and multiview data, current learning algorithms are designed on the basis of data characteristics, correlations, etc. While these algorithms cannot express correlations among different features, instances, labels in within-view, cross-view, and consensus-view representations self-adaptively and relative accurately. To this end, this study takes the classical multiple correlations-based model as the basis and explores some laws of self-adaptive change for those correlations in multiple representations. The proposed algorithm is called multiple self-adaptive correlation-based multiview multilabel learning (MuSC-MVML). Extensive experiments on 38 datasets demonstrate the superiority of MuSC-MVML and some conclusions are addressed. 1) MuSC-MVML outperforms most compared algorithms in statistical in terms of AUC and its performance is also stable; 2) the computational cost of MuSC-MVML is moderate and on most datasets, MuSC-MVML has a relatively fast convergence; and 3) introducing some laws of self-adaptive change for those correlations can improve the ability of MuSC-MVML to process multiview multilabel datasets effectively and express correlations in multiple representations better. Furthermore, this study explains the reason that why we use alternating optimization strategy to optimize the model of MuSC-MVML and provides some suggestions that how to modify the model of MuSC-MVML to process incomplete multiview multilabel datasets with noise.

No abstract is provided for this article.

Abstract The earliest detection of rumors across social media is the need to the hour in present global village. User’s are seamlessly connected in an unstructured network leading to rapid flow of information. User’s on the social media with malign intents may share defamatory content to contribute towards the fifth generation media warfare. The ingress of such defamatory content into society can result in panic, uncertainty and demoralization the peoples. Due to the huge amount of content over social platforms, the detection of malicious contents is hard. Earlier research while focuses on content profiling and flow of information, however, the reciprocal perspective of the source and following contents is missing. In this research, a novel Reciprocal Perspective fused Graph Convolutional Neural Network (RPf-GCN) is proposed. The proposed framework incorporates twin GCNs to encode both the bottom-up and top-down perspectives, enhancing the understanding of rumor propagation. Moreover convolutional operation is employed to fuse reciprocal perspective, providing a holistic view of the conversations. To validate the efficacy of the proposed framework, we conducted a series of experiments using real-world datasets, including PHEME and SemEval. Experimentation performed illustrates that the proposed framework outperformed over various baselines in two different evaluation metrics namely Macro F1 (for PHEME 0.736, for SemEval 0.461) and Accuracy (for PHEME 0.748, for SemEval 0.658).

Learning a proper distance for clustering from prior knowledge falls into the realm of semisupervised fuzzy clustering. Although most existing learning methods take prior knowledge (e.g., pairwise constraints) into account, they pay little attention to local knowledge of data, which, however, can be utilized to optimize the distance. In this article, we propose a novel distance learning method, which learns from the Group-level information, for semisupervised fuzzing clustering. We first present a new format of constraint information, called Group-level constraints, by elevating the pairwise constraints (must-links and cannot-links) from point level to Group level. The Groups, generated around data points contained in the pairwise constraints, carry not only the local information of data (the relation between close data points) but also more background information under some given limited prior knowledge. Then, we propose a novel method to learn a distance by using the Group-level constraints, namely, Group-based distance learning, in order to optimize the performance of fuzzy clustering. The distance learning process aims to pull must-link Groups as close as possible while pushing cannot-link Groups as far as possible. We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations. The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by using the neural network, which can explicitly provide nonlinear mappings. Experimental results based on both synthetic and real-world datasets show that the proposed methods yield much better performance compared to other distance learning methods using pairwise constraints.