Most multi-label domains lack an authoritative taxonomy. Therefore, different taxonomies are commonly used in the same domain, which results in complications. Although this situation occurs frequently, there has been little study of it using a principled statistical approach. Given that (1) different taxonomies used in the same domain are generally founded on the same latent semantic space, whe...

Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propa...

In multi-label learning, the relationship among labels is well accepted to be important, and various methods have been proposed to exploit label relationships. Amongst them, ensemble of classifier chains (ECC) which builds multiple chaining classifiers by random label orders has drawn much attention. However, the ensembles generated by ECC are often unnecessarily large, leading to extra high co...

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model le...

The biosynthesis of the plasmalogen forms of phosphatidylethanolamine (plasmenylethanolamine) and phosphatidylglycerol (plasmenylglycerol) and of the glycerol acetal of plasmenylethanolamine has been studied in cultures of Clostridium butyricum IFO 3852. When growing cells were pulsed with [32P]orthophosphate, there was a lag of 5 to 7 min between the rapid incorporation of label into the acylp...

Partners in VOs can share large amount of data. Sharing of individual data items is straightforward, but sharing components of complex data structures stored in heterogeneous systems is often a challenge. Sharing is typically governed by rules and policies that need to be translated into access right / privilege control and data granularity control. Simultaneous control of privileges and data g...

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. One way of learning underlying structures over labels is to project both instances and labels into the same space where an instance and its relevant labels tend to have similar representations. In this paper, we present a novel method to learn a joint sp...

Systems that automatically discover semantic classes have emerged in part to address the limitations of broad-coverage lexical resources such as WordNet and Cyc. The current state of the art discovers many semantic classes but fails to label their concepts. We propose an algorithm labeling semantic classes and for leveraging them to extract is-a relationships using a top-down approach.

In this paper, we discuss a general methodology for analysis and modeling of trust relationships in distributed computing. We discuss the classification of trust relationships, categorize trust relationships into two layers and provide a hierarchy of trust relationships based on a formal definition of trust relationship. We provide guidelines for the analysis and modeling of trust relationships...