The graph neural network (GNN) is a type of powerful deep learning model used to process data consisting nodes and edges. Many studies GNNs have modeled the relationships between edges labels only by homophily/heterophily, where most/few with same label tend an edge each other. However, this modeling method cannot describe multiconnection mode on graphs homophily can coexist heterophily. In wor...

Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample linked to multiple kinds of features and candidate labels, including ground-truth noise labels. The key MVPML how manipulate the recover labels from label set. To this end, study designs novel Graph-based Partial Multi-label model named as GMPM, which combines multi-view information detection, valuable s...

the matrix functions appear in several applications in engineering and sciences. the computation of these functions almost involved complicated theory. thus, improving the concept theoretically seems unavoidable to obtain some new relations and algorithms for evaluating these functions. the aim of this paper is proposing some new reciprocal for the function of block anti diagonal matrices. more...

Multiple neighboring organs or structures captured in medical images are frequently represented by labeling the underlying image domain (e.g. labeling a brain image into white matter, gray matter, cerebrospinal fluid, etc). However, given the different sources of uncertainties in shape boundaries (e.g. tissue heterogeneity, partial volume effect, fuzzy image processing and segmentation results)...

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimen...

Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a real-valued prediction, receives the true label, and incurs the squared loss. The goal is to design an online learning algorithm with sublinear regret to the best sparse linear predictor in hindsight. Without any assumpt...

Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods req...

Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF is most often utilized as an unsupervised feature extractor followed by class...

Labeled data is often sparse in common learning scenarios, either because it is too time consuming or too expensive to obtain, while unlabeled data is almost always plentiful. This asymmetry is exacerbated in multi-label learning, where the labeling process is more complex than in the single label case. Although it is important to consider semisupervised methods for multi-label learning, as it ...