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Poster
High Dimensional Structured Superposition Models
Qilong Gu · Arindam Banerjee

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #94
in Wednesday Posters »

High dimensional superposition models characterize observations using parameters which can be written as a sum of multiple component parameters, each with its own structure, e.g., sum of low rank and sparse matrices. In this paper, we consider general superposition models which allow sum of any number of component parameters, and each component structure can be characterized by any norm. We present a simple estimator for such models, give a geometric condition under which the components can be accurately estimated, characterize sample complexity of the estimator, and give non-asymptotic bounds on the componentwise estimation error. We use tools from empirical processes and generic chaining for the statistical analysis, and our results, which substantially generalize prior work on superposition models, are in terms of Gaussian widths of suitable spherical caps.

Keywords: (Other) Statistics · Learning Theory · Sparsity and Feature Selection · Model Selection and Structure Learning · Regularization and Large Margin Methods
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Author Information

Qilong Gu (University of Minnesota)
Arindam Banerjee (University of Illinois Urbana-Champaign)

Arindam Banerjee is a Professor at the Department of Computer & Engineering and a Resident Fellow at the Institute on the Environment at the University of Minnesota, Twin Cities. His research interests are in machine learning, data mining, and applications in complex real-world problems in different areas including climate science, ecology, recommendation systems, text analysis, and finance. He has won several awards, including the NSF CAREER award (2010), the IBM Faculty Award (2013), and six best paper awards in top-tier conferences.

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