Advances in High Dimensional Big Data

2nd Workshop in conjunction with the IEEE BigData Conference 2016

https://sites.google.com/site/adhdbigdata2/

Dec. 5-8, 2016 @ Washington D.C., USA

Call for Papers

High dimensionality is inherent in applications involving text, audio, images and video as well as in many biomedical applications involving high-throughput data. Many applications involving relational or network data also produce massive high-dimensional data sets. To deal with the challenges in processing and analysing such data sets, a wide range of approaches are available. These include "large p, small n" settings, dimensionality reduction, clustering, manifold learning, random projections and etc. Such approaches are crucial in dealing with issues concerning statistical reliability, revealing and visualizing structure hidden by the high dimensionality and noise, as well as saving the computation and storage burden.

The purpose of this workshop is two-fold: first to highlight novel research addressing high dimensionality and at the same time bringing in contact prominent researchers and practitioners in the particular aspect of big data analysis. The dual keynote talks from both the academia and the industry emphasizes the importance of bridging the gap between state-of-the-art research and practical applications.

The workshop's interests range from applications involving high dimensional data to the theoretical aspects of the problem. In addition, there is a particular interest in techniques that take advantage of data-parallel/graph-parallel platforms to effectively handle truly large-scale real- world problems, and techniques that improve memory efficiency, a premium in streaming and distributed environments.

The topics of this workshop include, but are not limited to:

- "Large p, small n" settings

- Supervised/unsupervised/semi-supervised dimensionality reduction

- Large-scale network analysis

- Data clustering

- Random Projections for big data

- High-dimensional data streams

- Manifold learning for big data

- Kernel-based approaches for big data

- Non-negative matrix factorization for big data

- Big data applications involving high dimensionality

Important Dates 

Oct. 10, 2016: Due date for full workshop papers submission

Nov. 1, 2016: Notification of paper acceptance to authors

Nov. 15, 2016: Camera-ready of accepted papers 

We invite submissions of research papers covering topics relevant to the theme of the workshop. All submissions will be peer reviewed by three members of the PC, and accepted papers will be published in the workshop proceedings (IEEE Press). Detailed formatting and submission instructions are available at the workshop homepage.

On Behalf of the Organizers,

Sotiris Tasoulis