PIMS-CORDS SFU Operations Research Seminar: Nicholas Richardson
Topic
Optimization and Applications for Unsupervised Signal Demixing
Speakers
Details
Throughout scientific and commercial domains, we are often interested in separating mixed signals into their component sources. Supervised deep learning is state-of-the-art when large and well-labeled datasets can be used. But in many applications, large scale collection and labelling can be too impraticable, expensive, or behind copyright laws. This talk will explore a number of applications from sediment analysis, genome sequencing, and audio source separation that fall into the scarce data category. We will see a few approaches I have used to model and solve these problems such as sparse feature models and tensor factorizations. These unsupervised learning techniques avoid a training phase and have the advantage of adapting to the specific example at hand.