| What? | The CSCD Colloquium Series, co-sponsored by the UMSN Fogarty International Training Program for Strengthening Non-Communicable Disease Research and Training Capacity in Thailand |
| Presenter | Dr. Saeid Amiri (University of Nebraska) |
| Topic | Dr. Amiri is an expert on machine learning, clustering methods and statistical genetics. Working with CSCD investigators, Dr. Amiri is developing a new foundation for modeling, analysis and interpretation of complex, high-dimensional and incongruent Big Data. |
| Where | Palmer Commons, Great Lakes Room North |
| Date | Tuesday, April 21, 2015 |
| When | 4:00-5:00 PM |
| Abstract | Extraction of valuable information from Big data (n>>p) in high dimensions (p>>n) and the subsequent scientific inference using such derived information present considerable challenges in many medical, biological, social and data-driven sciences. In this talk, I will present statistical learning and unsupervised machine learning techniques for the low dimension data and discuss a new sub-space alternative approach. We will illustrate an extension method for higher-dimensions and big data based on random subspaces. We provide a series of arguments to justify the new technique and will provide examples involving real and simulated data to compare our method with other related techniques. |
| See also | Dr. S. Ejaz Ahmed's talk on 4/24/15 part of the micro Big Data Analytics workshop. |