Multi-view Learning: Approaches and Applications
Clustering and classification are two important frameworks of machine learning. However, both take a single data usually represented in a matrix form whose rows denote samples and whose columns denote features/attributes. Each element in the matrix, therefore, denote the expression of a sample in a particular feature. Data in the real world, however, may arise from multiple sources or have multiple aspects. For instance, an event described by a video and an audio, or a movie described by its cast and its plot. These multiple features sets describing the same even is referred to as a multi-view data and exploiting information from such data is known as multi-view learning.
In this talk, we will review popular methodologies to incorporate multiple views in both supervised and unsupervised learning. We also study some successful applications of multi-view learning and see the advantages of multi-view learning.
Syed Fawad Hussain obtained his MS degree in Computer Science from the prestigious Pierre and Marie Curie University (now Paris-Sorbonne University), a top 50 ranked university in the world (World Ranking #36), Paris, France, and a Ph.D. in Computer Science from the University of Grenoble (World Ranking #151-200), Grenoble, France.
He has worked on various academic-industrial projects during his educational career. Prior to joining GIK Institute, he was associated with the TIMC Research Labs, where he was involved in research related to defining similarity measures in a linked graph with application to text mining for social media networking as part of a project partly funded by the French National Research Association and Xerox Research Europe. During his MS research internship with the ERIC research labs in Lyon, France, he proposed a Personalized Health Anticipation Data Warehouse as a novel approach to healthcare management and preemptive response, mainly designed for the French National Football Team.
Dr. Fawad Hussain is an HEC approved Ph.D. supervisor, a professional member of the Association for Computing Machinery (ACM), a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Higher Education Commission(HEC) of Pakistan National Curriculum Review Committee for Computer Science, a reviewer for the HEC National Research Project for Universities (NRPU) and the Pakistan Science Foundation (PSF). During his academic career, he has been awarded an HEC foreign scholarship for Ph.D., a Google/IBM Grant based on his publication at the SIAM Data Mining Conference (SDM 2010, USA), and merit certificates for 1st position at the intermediate level.
Syed Fawad Hussain is a recipient of multiple research and teaching awards. He was awarded the nationwide Best University Teacher Award (BUTA) for the year 2015 by the Higher Education Commission of Pakistan. The award is conferred on the basis of teaching, research, and other scholarly work performed during the year 2015. At the institute level, he has been awarded the G.I.K Research Award. Since 2021, he has been nominated as an International Distinguished Scholar under COMSTECH.