Most common techniques for correlation analysis (e.g., canonical correlation analysis) require sufficiently large sample support, but in many applications only a limited number of samples are available. Correlation analysis with small sample sizes poses some unique challenges. In this talk, I will focus on the problem of determining the correlated components between two or more data sets when the number of samples from these data sets is extremely small. Applications are plentiful, and among them I will discuss the identification of weather patterns in climate science and analyzing the effects of extensive physical exercise on the autonomic nervous system.
Peter Schreier was born in Munich, Germany, in 1975. He received a Master of Science from the University of Notre Dame, IN, USA, in 1999, and a Ph.D. from the University of Colorado at Boulder, CO, USA, in 2003, both in electrical engineering. From 2004 until 2011, he was on the faculty of the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. Since 2011, he has been Chaired Professor of Signal and System Theory at Paderborn University, Germany. He has spent sabbatical semesters at the University of Hawaii at Manoa, Honolulu, HI, and Colorado State University, Ft. Collins, CO.
From 2008 until 2012, he was an Associate Editor of the IEEE Transactions on Signal Processing, from 2010 until 2014 a Senior Area Editor for the same Transactions, and from 2015 to 2018 an Associate Editor for the IEEE Signal Processing Letters. From 2009 until 2014, he was a member of the IEEE Technical Committee on Machine Learning for Signal Processing, and he currently serves on the IEEE Technical Committee on Signal Processing Theory and Methods. He is the Chair of the Steering Committee of the IEEE Signal Processing Society’s Data Science Initiative, and he serves on the IEEE SPS Regional Committee for Region 8. He was the General Chair of the 2018 IEEE Statistical Signal Processing Workshop in Freiburg, Germany.
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