Regularized weighted Chebyshev approximations for support estimation

Chien, I

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https://hdl.handle.net/2142/106187 Copy

Description

Title

Regularized weighted Chebyshev approximations for support estimation

Author(s)

Chien, I

Issue Date

2019-11-13

Director of Research (if dissertation) or Advisor (if thesis)

Milenkovic, Olgica

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Date of Ingest

2020-03-02T21:58:11Z

Keyword(s)

• Support estimation
• Chebyshev polynomials

Abstract

We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the stateof-the-art techniques in the area and outperforms them in practice. In particular, we present both theoretical and computer simulation results that illustrate the utility and performance improvements of our method. The theoretical analysis relies on introducing a new weighted Chebyshev polynomial approximation method, jointly optimizing the bias and variance components of the risk, and combining the weighted minmax polynomial approximation method with discretized semi-infinite programming solvers. Such a setting allows for casting the estimation problem as a linear program (LP) with a small number of variables and constraints that may be solved as efficiently as the original Chebyshev approximation problem. Our technique is tested on synthetic data and textual data (Shakespeare’s plays), and is used to address an important problem in computational biology - estimating the number of bacterial genera in the human gut. On synthetic datasets, for practically relevant sample sizes, we observe significant improvements in the value of the worst-case risk compared to existing methods. The same is true of the text data. For the bioinformatics application, using metagenomic data from the NIH Human Gut and the American Gut Microbiome Projects, we generate a list of frequencies of bacterial taxa that allows us to estimate the number of bacterial genera to ∼ 2300.

Graduation Semester

2019-12

Type of Resource

text

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http://hdl.handle.net/2142/106187

Copyright and License Information

Copyright I Chien 2019

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