平特五不中

Event

Pengqi Liu (平特五不中)

Wednesday, October 25, 2023 13:00to14:00
Burnside Hall Room 1214, 805 rue Sherbrooke Ouest, Montreal, QC, H3A 0B9, CA

Title: Regularization in Finite Mixture of Sparse GLMs with Ultra-High Dimensionality and Convergence of EM Algorithm

Abstract: Finite mixture of generalized linear regression models (FM-GLM) are used for analyzing data that arise from populations with unobserved heterogeneity. In recent applications of FM-GLM, data are often collected on a large number of features. However, fitting an FM-GLM to such high-dimensional data is numerically challenging. To cope with the high-dimensionality in estimation, it is often assumed that the model is sparse and only a handful of features are relevant to the analysis. Most of the existing development on sparse estimation is in the context of homogeneous regression or supervised learning problems. In this talk, I will discuss some of the challenges and recent computational and theoretical developments for sparse estimation in FM-GLM when the number of features can be in exponential order of the sample size. Moreover, I will discuss a modified EM algorithm to obtain the estimates in FM-GLM numerically. The convergence theory of the modified EM algorithm for finite mixture of Gaussian regression with Lasso penalty will also be studied.

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