Abstract & Topics:

Traditional machine learning methods have been commonly used for many applications, such as text classification, image recognition, and video tracking. For learning purposes, these data are often required to be represented as vectors. However, many other types of data objects in real-world applications, such as chemical compounds in bio pharmacy, brain regions in brain networks and users in social networks, contain rich feature vectors and structure information. Such a simple feature-vector representation inherently loses the structure information of the objects. In reality, objects may have complicated characteristics, depending on how the objects are assessed and characterized. Meanwhile the data may come from heterogeneous domains, such as traditional tabular-based data, sequential patterns, social networks, time series information, and semi-structured data. Novel machine learning methods are desired to discover meaningful knowledge in advanced applications from objects with complicated characteristics.

This special session expects to solicit contributions on the advanced machine learning methods and applications from complicated data environment.

Topics of interest include (but are not limited to):

- Supervised/Unsupervised/Semi-supervised Learning

- Semi-structured Learning

- Graph-based Learning

- Graph Classification/Clustering/ Streaming

- Multi-Graph Learning

- Deep Graph Learning

- Online Graph Learning

- Time Series Learning

- Complex Social Networks

- Multi-view/instance/ label Learning

- Heterogeneous Transfer Learning

- Web/Text/Image Mining

- Multimedia Learning

- Big Data Analytics for Social Media

- Big Data and the Internet of Things