k-Partite graph reinforcement and its application in multimedia information retrieval

Learning from networked examples in a k-partite graph

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked examples, i.e. examples sharing pieces of information (such as vertices or edges). We propose an efficient weighting method for learning from networked examples and show a sample error bound which is better tha...

Differentially Expressed Functional Connectivity Networks with K-partite Graph Topology

Emerging brain network studies suggest that interactions between various distributed neuronal populations may be characterized by an organized complex topological structure. Many brain diseases are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to identify network-level differentially expressed connections that have low f...

Multimedia Information Retrieval

Information Retrieval (IR) is an active research area for many years. Initially, the field focused on the efficient and effective processing of user information needs expressed as a set of keywords. In fact, this model is still active today (e.g., web search engines). However, large volumes of information are available in non-textual form, such as images, audio files and videos. The research fi...

Multimedia information retrieval

Information Fusion in Multimedia Information Retrieval

In retrieval, indexing and classification of multimedia data an efficient information fusion of the different modalities is essential for the system’s overall performance. Since information fusion, its influence factors and performance improvement boundaries have been lively discussed in the last years in different research communities, we will review their latest findings. They most importantl...