data disaggregation algorithms

PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views

Faisal M. Almutairi, Ph.D. Candidate, Electrical and Computer Engineering, University of Minnesota

Jan 8, 12:00 - 13:00

B1 L4 R4214

data disaggregation algorithms University of Minnesota

The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform the disaggregation task on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.