Minisymposium (ID: SS-IDAHDMD)

Interdisciplinary Data Analysis of High-Dimensional Multimodal Data

Xu (Sunny) Wang (Wilfrid Laurier University), Yan Yuan (University of Alberta)

As we collect more complex and high-dimensional data from different sources, analysis and interpretation can be challenging and require sophisticated analytic techniques. It may be no longer effective to independently apply methods from a specific discipline such as statistics, mathematics, or computing science. Developing a new methodology by integrating methods from multiple disciplines is likely to enhance our ability to detect hidden structures in complex data. In this special session, methods and techniques developed from different disciplines will be presented for model building, dimension reduction and prediction on high-dimensional multimodal data.

Please note the ID code assigned to your presentation (identical to the ID code of your accepted abstract). It is required for submitting your paper for the AMMCS-2019 Proceedings. Submission is not mandatory. All submitted papers will be refereed and only accepted papers will be published in the AMMCS-2019 Proceedings.

If you intend to submit your paper, please go to the AMMCS Proceedings Page. Follow exactly the Author Instructions accessible from that page.