STA / BST 290 Seminar Series
Thursday, January 8, 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: Irina Gaynanova (Cornell University)
Title: “Multi-group classification via sparse discriminant analysis”
Abstract: It has been observed that classical multivariate analysis tools perform poorly on modern datasets due to the presence of spurious correlations and over-selection of relevant features. In the literature these problems have been addressed separately, however their joint consideration can lead to significant improvements in terms of empirical performance and computational speed. In this talk I focus on multi-group discriminant analysis with motivating examples coming from genetics and metabolomics studies. The estimation problem is formulated using convex optimization framework, which allows the use of a computationally efficient block-coordinate descent algorithm. In addition to the computational aspects, I will discuss the theoretical guarantees on the variable selection and classification consistency. Finally, the proposed methodology is used to aid drug discovery in the study of tuberculosis.