Convex Co-clustering of Tensors

SpeakerDr. Eric Chi
Organization North Carolina State University
LocationEBIII, room 2213
Start Date September 22, 2017 11:45 AM
End Date September 22, 2017 1:00 PM

Abstract: Clustering is a fundamental unsupervised learning technique that aims to discover groups of objects in a dataset. Biclustering extends clustering to two dimensions where both observations and variables are grouped simultaneously, such as clustering both cancerous tumors and genes or both documents and words. In this work, we develop and study a convex formulation of the generalization of biclustering to co-clustering the modes of multiway arrays or tensors, the generalization of matrices. Our convex co-clustering (CoCo) estimator is guaranteed to obtain a unique global minimum of the formulation and generates an entire solution path of possible co-clusters governed by a single tuning parameter. We extensively study our method in several simulated settings, and also apply it to an online advertising dataset. We also provide a finite sample bound for the prediction error of our CoCo estimator. This is joint work with Brian Gaines, Wei Sun, and Hua Zhou.

Bio: Eric is an assistant professor in the Department of Statistics at North Carolina State University. He earned his PhD in statistics at Rice University. Prior to joining NC State, he was a postdoc in the Human Genetics department at UCLA and then a postdoc in the Digital Signal Processing group at Rice University. His research interests are in statistical machine learning and numerical optimization and their application to analyzing large and complicated modern data in biological science and engineering applications.

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