Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied Planted Bisection Model dyn-G(n, p, q) where the node set [n] of the network is partitioned into two unknown communities and, at every ...

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 attempt to make training and prediction tractable by assuming that the training label matrix is low-rank and reducing the effective number of labels by projecting the high dimens...

In this brief, we propose a novel multilabel learning framework, called multilabel self-paced learning, in an attempt to incorporate the SPL scheme into the regime of multilabel learning. Specifically, we first propose a new multilabel learning formulation by introducing a self-paced function as a regularizer, so as to simultaneously prioritize label learning tasks and instances in each iterati...

The elaboration of enamel matrix glycoprotein was investigated in secretory ameloblasts of incisor teeth in 30-40-g rats. To this end, the distribution of glycoprotein was examined histochemically by the use of phosphotungstic acid at low pH, while the formation of glycoprotein was traced radioautographically in animals sacrificed 2.5-30 min after galactose-(3)H injection. Histochemically, the ...

The mining of temporal aspects over multi-dimensional data is increasingly critical for healthcare planning tasks. A healthcare planning task is, in essence, a classification problem over health-related attributes across temporal horizons. The increasingly integration of healthcare data through multi-dimensional structures triggers new opportunities for an adequate long-term planning of resourc...

Feature selection has attracted considerable attention due to the wide application of multi-label learning. However, previous methods do not fully consider relationship between feature sets and label but devote either them. Furthermore, conventional learning utilizes logical labels estimate relevance so that importance corresponding cannot be well reflected. Additionally, numerous irrelevant re...

For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substan...