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--- seminars:datasci:200326 [2020/03/20 17:41] – created qyu
+++ seminars:datasci:200326 [2020/03/20 17:43] (current) – qyu
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+<WRAP centeralign>##Data Science Seminar##\\ Hosted by Department of Mathematical Sciences</WRAP>
+  * Date: Tuesday, March 26, 2020
+  * Time: 12:00pm -- 1:00pm
+  * Room: WH-100E
+  * Speaker: Wangshu Tu (Binghamton University)
+  * Title: A family of mixture models for biclustering
+<WRAP center box 80%>
+<WRAP centeralign>**//Abstract//**</WRAP>
+Biclustering allows for simultaneous clustering of the observations and
+variables. Martella et. al (2008) introduced biclustering in a model-based
+clustering framework by utilizing a structure similar to a mixture of
+factor analyzer structures such that observed variables are modelled using
+a latent variable that is assumed to be from a MVN(0, I). In Martella et.
+al (2008), clustering of variables was introduced by imposing constraints
+on the entries of the factor loading matrix to be 0 and 1. However, this
+approach restricts the non-zero off-diagonal entries of the covariance
+matrix to be 1, which is very restrictive. Here, we assume the latent
+variable to be from a MVN(0,T) where T is a diagonal matrix and hence, the
+non-zero off-diagonal entires of the covariance matrix are not restricted
+to be equal to 1. A family of models are developed by imposing constraints
+on the components of the covariance matrix. An alternating expectation
+conditional maximization(AECM) algorithm is used for parameter estimation.
+Proposed method will be illustrated using simulated and real datasets. The
+presentation will conclude with some on-going work and future research
+directions.
+</WRAP>