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Multiview Feature Selection for Single-view Classification

March 10, 2021 at 2:00 PM to 3:00 PM

Data Science Distinguished Speaker Seminar Series  

Title: Multiview Feature Selection for Single-view Classification
Presented by: Dr. Majid Komeili
Format: Online via Zoom

Abstract: In many real-world scenarios, data from multiple modalities (sources) are collected during a development phase. Such data are referred to as multi-view data. While additional information from multiple views often improves the performance, collecting data from such additional views during the testing phase may not be desired due to the high costs associated with measuring such views or, unavailability of such additional views. Therefore, in many applications, despite having a multi-view training data set, it is desired to do performance testing using data from only one view. I will present a multi-view feature selection method for transferring the knowledge of all views to improve the feature selection process in an individual view and discuss its application in classification of microarray data.

About the Speaker: Dr. Komeili is an Assistant Professor at the School of Computer Science and the Institute for Data Science at Carleton University. He performs fundamental and applied research in machine learning. Before joining Carleton University, Dr. Komeili completed his Ph.D. in Electrical and Computer Engineering at the University of Toronto followed by a postdoctoral fellowship at Vector Institute and Toronto Rehabilitation institute.

Seminar Moderator: Dr. Tracey P. Lauriault, Associate Professor, Critical Media and Big Data, School of Journalism and Communication, Carleton University

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