Label propagation (LP) is a popular graph-based semi-supervised learning framework. Its effectiveness limited by the distribution of prior labels. If there are no objects with labels in parts classes, label has very poor performance. To address this issue, we propose based on bipartite graph (LPBBG) algorithm. This approach try to learn as exemplar constraints that reflect relations between and...

Abstract Approximate message passing (AMP) algorithms have become an important element of high-dimensional statistical inference, mostly due to their adaptability and concentration properties, the state evolution (SE) equations. This is demonstrated by growing number new iterations proposed for increasingly complex problems, ranging from multi-layer inference low-rank matrix estimation with ela...

Knowledge bases built in the knowledge processing field have a problem that experts to add rules or update them through modifications. To solve this problem, research has been conducted on graph expansion methods using deep learning technology, and recent years, many studies of generating by embedding graph’s triple information continuous vector space. In paper, literature summary, we propose d...