The problem of community detection receives great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimate of this model is equivalent to label propagation wi...

Multi-label classification aims to recognize multiple objects or attributes from images. The key solving this issue relies on effectively characterizing the inter-label correlations dependencies, which bring prevailing graph neural network. However, current methods often use co-occurrence probability of labels based training set as adjacency matrix model correlation, is greatly limited by datas...

We present a scalable Bayesian multi-label learning model based on learning lowdimensional label embeddings. Our model assumes that each label vector is generated as a weighted combination of a set of topics (each topic being a distribution over labels), where the combination weights (i.e., the embeddings) for each label vector are conditioned on the observed feature vector. This construction, ...

the efficient detection of charged biomolecules by biosensor with appropriate semiconducting nanomaterials and with optimum device geometry has caught tremendous research interest in the present decade. here, the performance of various label-free electronic biosensors to detect bio-molecules is investigated by simulation technique. silicon nanowire sensor, nanosphere sensor and double gate fiel...

In reality, like single-label data, multi-label data sets have the problem that only some labels. This is an excellent challenge for feature selection. paper combines logistic regression model with graph regularization and sparse to form a joint framework (SMLFS) semi-supervised First of all, used explore geometry structure feature, obtain better coefficient matrix, which reflects importance fe...

Large classifier systems are machine learning algorithms that use multiple classifiers to improve the prediction of target values in advanced classification tasks. Although learning problems in bioand cheminformatics commonly provide data in schemes suitable for large classifier systems, they are rarely used in these domains. This thesis introduces two new classifiers incorporating systems of c...

The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (ent...

Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utiliz...

We pose transductive classification as a matrix completion problem. By assuming the underlying matrix has a low rank, our formulation is able to handle three problems simultaneously: i) multi-label learning, where each item has more than one label, ii) transduction, where most of these labels are unspecified, and iii) missing data, where a large number of features are missing. We obtained satis...

The transport of cytoplasmically synthesized mitochondrial proteins was investigated in whole cells of Neurospora crassa, using dual labelling and immunological techniques. In pulse and pulse-chase labelling experiments the mitochondrial proteins accumulate label. The appearance of label in mitochondrial protein shows a lag relative to total cellular protein, ribosomal, microsomal and cytosolic...