Lifang He

TL;DR: The proposed MIC (Multi-Incomplete-view Clustering), an algorithm based on weighted nonnegative matrix factorization with L2,1 regularization, works by learning the latent feature matrices for all the views and generating a consensus matrix so that the difference between each view and the consensus is minimized.

TL;DR: A novel linear support higher-order tensor machine (SHTM) which integrates the merits of linear C-support vector machine (C-SVM) and tensor rank-one decomposition and provides significant performance gain in terms of test accuracy and training speed, especially in the case of higher- order tensors.

TL;DR: This paper proposes to use graph convolutional policy network based on reinforcement learning to generate dynamic graphs when the dynamic graphs are incomplete due to the data sparsity, and demonstrates that the model can achieve stable and effective long-term predictions of traffic flow, and can reduce the impact of data defects on prediction results.

TL;DR: The proposed RLS-SVM significantly reduces the effect of the noise in the training dataset and provides superior robustness and an iterative algorithm based on the concave–convex procedure (CCCP) and the Newton algorithm is proposed.

TL;DR: Zhang et al. as discussed by the authors proposed a hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification, where each document was modeled as a word order preserved graph-of-words and normalized it as a corresponding word matrix representation preserving both the non-consecutive, long-distance and local sequential semantics.