Generalized Spectral Analysis for Large Sets of Approval Voting Data

Generalized Spectral Analysis for Large Sets of Approval Voting Data

Generalized Spectral analysis of approval voting data uses representation theory and the symmetry of the data to project the approval voting data into orthogonal and interpretable subspaces. Unfortunately, as the number of voters grows, the data space becomes prohibitively large to compute the decom...

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Bibliographic Details
Main Author: Uminsky, David
Format: Others
Published: Scholarship @ Claremont 2003
Subjects:
Spectral Analysis
Voting
Online Access:https://scholarship.claremont.edu/hmc_theses/157
https://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1160&context=hmc_theses
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spelling ndltd-CLAREMONT-oai-scholarship.claremont.edu-hmc_theses-11602019-10-16T03:06:33Z Generalized Spectral Analysis for Large Sets of Approval Voting Data Uminsky, David Generalized Spectral analysis of approval voting data uses representation theory and the symmetry of the data to project the approval voting data into orthogonal and interpretable subspaces. Unfortunately, as the number of voters grows, the data space becomes prohibitively large to compute the decomposition of the data vector. To attack these large data sets we develop a method to partition the data set into equivalence classes, in order to drastically reduce the size of the space while retaining the necessary characteristics of the data set. We also make progress on the needed statistical tools to explain the results of the spectral analysis. The standard spectral analysis will be demonstrated, and our partitioning technique is applied to U.S. Senate roll call data. 2003-05-01T07:00:00Z text application/pdf https://scholarship.claremont.edu/hmc_theses/157 https://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1160&context=hmc_theses HMC Senior Theses Scholarship @ Claremont Spectral Analysis Voting
collection NDLTD
format Others
sources NDLTD
topic Spectral Analysis
Voting
spellingShingle Spectral Analysis
Voting
Uminsky, David
Generalized Spectral Analysis for Large Sets of Approval Voting Data
description Generalized Spectral analysis of approval voting data uses representation theory and the symmetry of the data to project the approval voting data into orthogonal and interpretable subspaces. Unfortunately, as the number of voters grows, the data space becomes prohibitively large to compute the decomposition of the data vector. To attack these large data sets we develop a method to partition the data set into equivalence classes, in order to drastically reduce the size of the space while retaining the necessary characteristics of the data set. We also make progress on the needed statistical tools to explain the results of the spectral analysis. The standard spectral analysis will be demonstrated, and our partitioning technique is applied to U.S. Senate roll call data.
author Uminsky, David
author_facet Uminsky, David
author_sort Uminsky, David
title Generalized Spectral Analysis for Large Sets of Approval Voting Data
title_short Generalized Spectral Analysis for Large Sets of Approval Voting Data
title_full Generalized Spectral Analysis for Large Sets of Approval Voting Data
title_fullStr Generalized Spectral Analysis for Large Sets of Approval Voting Data
title_full_unstemmed Generalized Spectral Analysis for Large Sets of Approval Voting Data
title_sort generalized spectral analysis for large sets of approval voting data
publisher Scholarship @ Claremont
publishDate 2003
url https://scholarship.claremont.edu/hmc_theses/157
https://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1160&context=hmc_theses
work_keys_str_mv AT uminskydavid generalizedspectralanalysisforlargesetsofapprovalvotingdata
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